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Galvan CM, Spies RD, Milone DH, Peterson V. Neurophysiologically Meaningful Motor Imagery EEG Simulation With Applications to Data Augmentation. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2346-2355. [PMID: 38900612 DOI: 10.1109/tnsre.2024.3417311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Motor imagery-based Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention due to their potential usability in neurorehabilitation and neuroprosthetics. However, the accurate recognition of MI patterns in electroencephalography signals (EEG) is hindered by several data-related limitations, which restrict the practical utilization of these systems. Moreover, leveraging deep learning (DL) models for MI decoding is challenged by the difficulty of accessing user-specific MI-EEG data on large scales. Simulated MI-EEG signals can be useful to address these issues, providing well-defined data for the validation of decoding models and serving as a data augmentation approach to improve the training of DL models. While substantial efforts have been dedicated to implementing effective data augmentation strategies and model-based EEG signal generation, the simulation of neurophysiologically plausible EEG-like signals has not yet been exploited in the context of data augmentation. Furthermore, none of the existing approaches have integrated user-specific neurophysiological information during the data generation process. Here, we present PySimMIBCI, a framework for generating realistic MI-EEG signals by integrating neurophysiologically meaningful activity into biophysical forward models. By means of PySimMIBCI, different user capabilities to control an MI-BCI can be simulated and fatigue effects can be included in the generated EEG. Results show that our simulated data closely resemble real data. Moreover, a proposed data augmentation strategy based on our simulated user-specific data significantly outperforms other state-of-the-art augmentation approaches, enhancing DL models performance by up to 15%.
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Mustile M, Kourtis D, Edwards MG, Donaldson DI, Ietswaart M. Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences. Front Hum Neurosci 2024; 18:1412307. [PMID: 38974480 PMCID: PMC11224467 DOI: 10.3389/fnhum.2024.1412307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 05/20/2024] [Indexed: 07/09/2024] Open
Abstract
A large body of evidence shows that motor imagery and action execution behaviors result from overlapping neural substrates, even in the absence of overt movement during motor imagery. To date it is unclear how neural activations in motor imagery and execution compare for naturalistic whole-body movements, such as walking. Neuroimaging studies have not directly compared imagery and execution during dynamic walking movements. Here we recorded brain activation with mobile EEG during walking compared to during imagery of walking, with mental counting as a control condition. We asked 24 healthy participants to either walk six steps on a path, imagine taking six steps, or mentally count from one to six. We found beta and alpha power modulation during motor imagery resembling action execution patterns; a correspondence not found performing the control task of mental counting. Neural overlap occurred early in the execution and imagery walking actions, suggesting activation of shared action representations. Remarkably, a distinctive walking-related beta rebound occurred both during action execution and imagery at the end of the action suggesting that, like actual walking, motor imagery involves resetting or inhibition of motor processes. However, we also found that motor imagery elicits a distinct pattern of more distributed beta activity, especially at the beginning of the task. These results indicate that motor imagery and execution of naturalistic walking involve shared motor-cognitive activations, but that motor imagery requires additional cortical resources.
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Affiliation(s)
- Magda Mustile
- Department of Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
- The Psychological Sciences Research Institute, University of Louvain, Louvain-la-Neuve, Belgium
| | - Dimitrios Kourtis
- Department of Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
| | - Martin G. Edwards
- The Psychological Sciences Research Institute, University of Louvain, Louvain-la-Neuve, Belgium
| | - David I. Donaldson
- School of Psychology and Neuroscience, University of St Andrews, St. Andrews, United Kingdom
| | - Magdalena Ietswaart
- Department of Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, United Kingdom
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Sun H, Ding Y, Bao J, Qin K, Tong C, Jin J, Guan C. Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention. Neural Netw 2024; 178:106470. [PMID: 38943861 DOI: 10.1016/j.neunet.2024.106470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/29/2024] [Accepted: 06/16/2024] [Indexed: 07/01/2024]
Abstract
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification performance and require significant time for new users to collect subject-specific training data. This limitation diminishes the user-friendliness of BCIs and presents significant challenges in developing effective subject-independent models. In response to these challenges, we propose a novel subject-independent framework for learning temporal dependency for motor imagery BCIs by Contrastive Learning and Self-attention (CLS). In CLS model, we incorporate self-attention mechanism and supervised contrastive learning into a deep neural network to extract important information from electroencephalography (EEG) signals as features. We evaluate the CLS model using two large public datasets encompassing numerous subjects in a subject-independent experiment condition. The results demonstrate that CLS outperforms six baseline algorithms, achieving a mean classification accuracy improvement of 1.3 % and 4.71 % than the best algorithm on the Giga dataset and OpenBMI dataset, respectively. Our findings demonstrate that CLS can effectively learn invariant discriminative features from training data obtained from non-target subjects, thus showcasing its potential for building models for new users without the need for calibration.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Yi Ding
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jianzhu Bao
- School of Computer Science and Technology, Harbin Insitute of Technology, Shenzhen, China
| | - Ke Qin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Chengxuan Tong
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China; Shenzhen Research Institute of East China University of Technology, Shen Zhen 518063, China.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
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Sengupta P, Lakshminarayanan K. Motor imagery of finger movements: Effects on cortical and muscle activities. Behav Brain Res 2024; 471:115100. [PMID: 38852744 DOI: 10.1016/j.bbr.2024.115100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/06/2024] [Accepted: 06/06/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE The purpose of the current study was to explore the immediate effect of motor imagery (MI) involving finger movement of a given limb on cortical response and muscle activity in healthy subjects. METHODS Twenty healthy right-handed adults (7 females and 13 males) with a mean + SD age of 22.05 + 6.08 years participated in the study. The beta-band event-related desynchronization (ERD) at the sensorimotor cortex and muscle activity during finger movement tasks using either the index, middle, or thumb digits on the non-dominant left hand were compared before and after an MI training session. Subjects underwent a pre-MI, MI training, and finally a post-MI session where they either performed or imagined performing a button-pushing action 50 times per session with each of the three digits. RESULTS The ERD power in the beta frequency band was lower in pre-MI compared to post-MI and was significantly different between the pre- and post-MI sessions for both the index and middle fingers, but not the thumb. A significant decrease was seen in the mean muscle activity during post-MI compared to pre-MI for all the digits except the thumb. CONCLUSIONS The results from the current study suggest that complex MI can result in motor learning and improvement in motor performance, thereby requiring less effort during motion.
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Affiliation(s)
- Puja Sengupta
- Neuro-rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kishor Lakshminarayanan
- Neuro-rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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De Sanctis P, Mahoney JR, Wagner J, Blumen HM, Mowrey W, Ayers E, Schneider C, Orellana N, Molholm S, Verghese J. Linking Dementia Pathology and Alteration in Brain Activation to Complex Daily Functional Decline During the Preclinical Dementia Stages: Protocol for a Prospective Observational Cohort Study. JMIR Res Protoc 2024; 13:e56726. [PMID: 38842914 PMCID: PMC11190628 DOI: 10.2196/56726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Progressive difficulty in performing everyday functional activities is a key diagnostic feature of dementia syndromes. However, not much is known about the neural signature of functional decline, particularly during the very early stages of dementia. Early intervention before overt impairment is observed offers the best hope of reducing the burdens of Alzheimer disease (AD) and other dementias. However, to justify early intervention, those at risk need to be detected earlier and more accurately. The decline in complex daily function (CdF) such as managing medications has been reported to precede impairment in basic activities of daily living (eg, eating and dressing). OBJECTIVE Our goal is to establish the neural signature of decline in CdF during the preclinical dementia period. METHODS Gait is central to many CdF and community-based activities. Hence, to elucidate the neural signature of CdF, we validated a novel electroencephalographic approach to measuring gait-related brain activation while participants perform complex gait-based functional tasks. We hypothesize that dementia-related pathology during the preclinical period activates a unique gait-related electroencephalographic (grEEG) pattern that predicts a subsequent decline in CdF. RESULTS We provide preliminary findings showing that older adults reporting CdF limitations can be characterized by a unique gait-related neural signature: weaker sensorimotor and stronger motor control activation. This subsample also had smaller brain volume and white matter hyperintensities in regions affected early by dementia and engaged in less physical exercise. We propose a prospective observational cohort study in cognitively unimpaired older adults with and without subclinical AD (plasma amyloid-β) and vascular (white matter hyperintensities) pathologies. We aim to (1) establish the unique grEEG activation as the neural signature and predictor of decline in CdF during the preclinical dementia period; (2) determine associations between dementia-related pathologies and incidence of the neural signature of CdF; and (3) establish associations between a dementia risk factor, physical inactivity, and the neural signature of CdF. CONCLUSIONS By establishing the clinical relevance and biological basis of the neural signature of CdF decline, we aim to improve prediction during the preclinical stages of ADs and other dementias. Our approach has important research and translational implications because grEEG protocols are relatively inexpensive and portable, and predicting CdF decline may have real-world benefits. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/56726.
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Affiliation(s)
- Pierfilippo De Sanctis
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Pediatrics, Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Jeannette R Mahoney
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Johanna Wagner
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, United States
| | - Helena M Blumen
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wenzhu Mowrey
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Emmeline Ayers
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Claudia Schneider
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Natasha Orellana
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Sophie Molholm
- Department of Pediatrics, Cognitive Neurophysiology Laboratory, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Joe Verghese
- Department of Neurology, Division of Cognitive and Motor Aging, Albert Einstein College of Medicine, Bronx, NY, United States
- Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, United States
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Miao M, Yang Z, Sheng Z, Xu B, Zhang W, Cheng X. Multi-source deep domain adaptation ensemble framework for cross-dataset motor imagery EEG transfer learning. Physiol Meas 2024; 45:055024. [PMID: 38772402 DOI: 10.1088/1361-6579/ad4e95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Objective. Electroencephalography (EEG) is an important kind of bioelectric signal for measuring physiological activities of the brain, and motor imagery (MI) EEG has significant clinical application prospects. Convolutional neural network has become a mainstream algorithm for MI EEG classification, however lack of subject-specific data considerably restricts its decoding accuracy and generalization performance. To address this challenge, a novel transfer learning (TL) framework using auxiliary dataset to improve the MI EEG classification performance of target subject is proposed in this paper.Approach. We developed a multi-source deep domain adaptation ensemble framework (MSDDAEF) for cross-dataset MI EEG decoding. The proposed MSDDAEF comprises three main components: model pre-training, deep domain adaptation, and multi-source ensemble. Moreover, for each component, different designs were examined to verify the robustness of MSDDAEF.Main results. Bidirectional validation experiments were performed on two large public MI EEG datasets (openBMI and GIST). The highest average classification accuracy of MSDDAEF reaches 74.28% when openBMI serves as target dataset and GIST serves as source dataset. While the highest average classification accuracy of MSDDAEF is 69.85% when GIST serves as target dataset and openBMI serves as source dataset. In addition, the classification performance of MSDDAEF surpasses several well-established studies and state-of-the-art algorithms.Significance. The results of this study show that cross-dataset TL is feasible for left/right-hand MI EEG decoding, and further indicate that MSDDAEF is a promising solution for addressing MI EEG cross-dataset variability.
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Affiliation(s)
- Minmin Miao
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou, People's Republic of China
| | - Zhong Yang
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
| | - Zhenzhen Sheng
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou, People's Republic of China
| | - Baoguo Xu
- School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Wenbin Zhang
- College of Computer Science and Software Engineering, Hohai University, Nanjing, Jiangsu Province, People's Republic of China
| | - Xinmin Cheng
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou, People's Republic of China
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Liu L, Li J, Ouyang R, Zhou D, Fan C, Liang W, Li F, Lv Z, Wu X. Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton. J Neurosci Methods 2024; 406:110132. [PMID: 38604523 DOI: 10.1016/j.jneumeth.2024.110132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/11/2024] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. NEW METHOD Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. COMPARISON WITH EXISTING METHODS In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. RESULTS In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. CONCLUSION Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.
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Affiliation(s)
- Lei Liu
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Jian Li
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Rui Ouyang
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China
| | - Danya Zhou
- National Centre for International Research in Cell and Gene Therapy, School of Basic Medical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, China
| | - Cunhang Fan
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China.
| | - Wen Liang
- Google Inc, United States of America
| | - Fan Li
- Civil Aviation Flight University of China, China
| | - Zhao Lv
- Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China; Civil Aviation Flight University of China, China
| | - Xiaopei Wu
- School of Computer Science and Technology, Anhui University, Hefei 230601, China; Anhui Province Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei 230601, China.
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Tien HP, Chang EC. Inequivalent and uncorrelated response priming in motor imagery and execution. Front Psychol 2024; 15:1363495. [PMID: 38860046 PMCID: PMC11163096 DOI: 10.3389/fpsyg.2024.1363495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 05/13/2024] [Indexed: 06/12/2024] Open
Abstract
Introduction Theoretical considerations on motor imagery and motor execution have long been dominated by the functional equivalence view. Previous empirical works comparing these two modes of actions, however, have largely relied on subjective judgments on the imagery process, which may be exposed to various biases. The current study aims to re-examine the commonality and distinguishable aspects of motor imagery and execution via a response repetition paradigm. This framework aims to offer an alternative approach devoid of self-reporting, opening the opportunity for less subjective evaluation of the disparities and correlations between motor imagery and motor execution. Methods Participants performed manual speeded-choice on prime-probe pairs in each trial under three conditions distinguished by the modes of response on the prime: mere observation (Perceptual), imagining response (Imagery), and actual responses (Execution). Responses to the following probe were all actual execution of button press. While Experiment 1 compared the basic repetition effects in the three prime conditions, Experiment 2 extended the prime duration to enhance the quality of MI and monitored electromyography (EMG) for excluding prime imagery with muscle activities to enhance specificity of the underlying mechanism. Results In Experiment 1, there was no significant repetition effect after mere observation. However, significant repetition effects were observed in both imagery and execution conditions, respectively, which were also significantly correlated. In Experiment 2, trials with excessive EMG activities were excluded before further statistical analysis. A consistent repetition effect pattern in both Imagery and Execution but not the Perception condition. Now the correlation between Imagery and Execution conditions were not significant. Conclusion Findings from the current study provide a novel application of a classical paradigm, aiming to minimize the subjectivity inherent in imagery assessments while examining the relationship between motor imagery and motor execution. By highlighting differences and the absence of correlation in repetition effects, the study challenges the functional equivalence hypothesis of imagery and execution. Motor representations of imagery and execution, when measured without subjective judgments, appear to be more distinguishable than traditionally thought. Future studies may examine the neural underpinnings of the response repetition paradigm to further elucidating the common and separable aspects of these two modes of action.
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Affiliation(s)
- Hsin-Ping Tien
- Action and Cognition Laboratory, Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan, Taiwan
- Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Central University and Academia Sinica, Taipei, Taiwan
| | - Erik C. Chang
- Action and Cognition Laboratory, Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan, Taiwan
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Liu R, Chen Y, Li A, Ding Y, Yu H, Guan C. Aggregating intrinsic information to enhance BCI performance through federated learning. Neural Netw 2024; 172:106100. [PMID: 38232427 DOI: 10.1016/j.neunet.2024.106100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/20/2023] [Accepted: 01/03/2024] [Indexed: 01/19/2024]
Abstract
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices. The significance of this challenge cannot be overstated, given the critical role of data diversity in fostering model robustness. However, existing works rarely discuss this issue, predominantly centering their attention on model training within a single dataset, often in the context of inter-subject or inter-session settings. In this work, we propose a hierarchical personalized Federated Learning EEG decoding (FLEEG) framework to surmount this challenge. This innovative framework heralds a new learning paradigm for BCI, enabling datasets with disparate data formats to collaborate in the model training process. Each client is assigned a specific dataset and trains a hierarchical personalized model to manage diverse data formats and facilitate information exchange. Meanwhile, the server coordinates the training procedure to harness knowledge gleaned from all datasets, thus elevating overall performance. The framework has been evaluated in Motor Imagery (MI) classification with nine EEG datasets collected by different devices but implementing the same MI task. Results demonstrate that the proposed framework can boost classification performance up to 8.4% by enabling knowledge sharing between multiple datasets, especially for smaller datasets. Visualization results also indicate that the proposed framework can empower the local models to put a stable focus on task-related areas, yielding better performance. To the best of our knowledge, this is the first end-to-end solution to address this important challenge.
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Affiliation(s)
- Rui Liu
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Yuanyuan Chen
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Anran Li
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Yi Ding
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Han Yu
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore.
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Sengupta P, Lakshminarayanan K. Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery. Behav Brain Res 2024; 459:114760. [PMID: 37979923 DOI: 10.1016/j.bbr.2023.114760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.
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Affiliation(s)
- Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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Papadopoulos S, Szul MJ, Congedo M, Bonaiuto JJ, Mattout J. Beta bursts question the ruling power for brain-computer interfaces. J Neural Eng 2024; 21:016010. [PMID: 38167234 DOI: 10.1088/1741-2552/ad19ea] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/05/2024]
Abstract
Objective: Current efforts to build reliable brain-computer interfaces (BCI) span multiple axes from hardware, to software, to more sophisticated experimental protocols, and personalized approaches. However, despite these abundant efforts, there is still room for significant improvement. We argue that a rather overlooked direction lies in linking BCI protocols with recent advances in fundamental neuroscience.Approach: In light of these advances, and particularly the characterization of the burst-like nature of beta frequency band activity and the diversity of beta bursts, we revisit the role of beta activity in 'left vs. right hand' motor imagery (MI) tasks. Current decoding approaches for such tasks take advantage of the fact that MI generates time-locked changes in induced power in the sensorimotor cortex and rely on band-passed power changes in single or multiple channels. Although little is known about the dynamics of beta burst activity during MI, we hypothesized that beta bursts should be modulated in a way analogous to their activity during performance of real upper limb movements.Main results and Significance: We show that classification features based on patterns of beta burst modulations yield decoding results that are equivalent to or better than typically used beta power across multiple open electroencephalography datasets, thus providing insights into the specificity of these bio-markers.
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Affiliation(s)
- Sotirios Papadopoulos
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Maciej J Szul
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Marco Congedo
- GIPSA-lab, University Grenoble Alpes, CNRS, Grenoble-INP, Grenoble, France
| | - James J Bonaiuto
- University Lyon 1, Lyon, France
- Institut de Sciences Cognitives Marc Jeannerod, CNRS, UMR5229, Lyon, France
| | - Jérémie Mattout
- University Lyon 1, Lyon, France
- Lyon Neuroscience Research Center, CRNL, INSERM U1028, CNRS, UMR5292, Lyon, France
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12
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Syrov N, Yakovlev L, Kaplan A, Lebedev M. Motor cortex activation during visuomotor transformations: evoked potentials during overt and imagined movements. Cereb Cortex 2024; 34:bhad440. [PMID: 37991276 DOI: 10.1093/cercor/bhad440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 11/23/2023] Open
Abstract
Despite the prevalence of visuomotor transformations in our motor skills, their mechanisms remain incompletely understood, especially when imagery actions are considered such as mentally picking up a cup or pressing a button. Here, we used a stimulus-response task to directly compare the visuomotor transformation underlying overt and imagined button presses. Electroencephalographic activity was recorded while participants responded to highlights of the target button while ignoring the second, non-target button. Movement-related potentials (MRPs) and event-related desynchronization occurred for both overt movements and motor imagery (MI), with responses present even for non-target stimuli. Consistent with the activity accumulation model where visual stimuli are evaluated and transformed into the eventual motor response, the timing of MRPs matched the response time on individual trials. Activity-accumulation patterns were observed for MI, as well. Yet, unlike overt movements, MI-related MRPs were not lateralized, which appears to be a neural marker for the distinction between generating a mental image and transforming it into an overt action. Top-down response strategies governing this hemispheric specificity should be accounted for in future research on MI, including basic studies and medical practice.
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Affiliation(s)
- Nikolay Syrov
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1. Moscow, 121205, Russia
| | - Lev Yakovlev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1. Moscow, 121205, Russia
| | - Alexander Kaplan
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1. Moscow, 121205, Russia
- Faculty of Biology, Lomonosov Moscow State University, 1-12 Leninskie Gory, Moscow, 119991, Russia
| | - Mikhail Lebedev
- Faculty of Mechanics and Mathematics, Lomonosov Moscow State University, 1 Leninskiye Gory, Moscow, 119991, Russia
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13
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Jorajuria T, Nikulin VV, Kapralov N, Gomez M, Vidaurre C. MEAN SP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI? IEEE Trans Neural Syst Rehabil Eng 2023; 31:4931-4941. [PMID: 38051627 DOI: 10.1109/tnsre.2023.3339612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called [Formula: see text] to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. [Formula: see text] has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.
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14
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Rayson H, Szul MJ, El-Khoueiry P, Debnath R, Gautier-Martins M, Ferrari PF, Fox N, Bonaiuto JJ. Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood. J Neurosci 2023; 43:8487-8503. [PMID: 37833066 PMCID: PMC10711718 DOI: 10.1523/jneurosci.0886-23.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/15/2023] [Accepted: 07/20/2023] [Indexed: 10/15/2023] Open
Abstract
Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.
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Affiliation(s)
- Holly Rayson
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
- Inovarion, Paris, 75005, France
| | - Maciej J Szul
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Perla El-Khoueiry
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Ranjan Debnath
- Center for Psychiatry and Psychotherapy, Justus-Liebig University, Giessen, 35394, Germany
| | - Marine Gautier-Martins
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Pier F Ferrari
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
| | - Nathan Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
| | - James J Bonaiuto
- Institut des Sciences, Cognitives Marc Jeannerod, Centre National de la Recherche Scientifique Unité Mixte de Recherche 5229, Bron, 69500, France
- Université de Lyon, Université Claude Bernard Lyon 1, Lyon, 69100, France
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15
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Miao M, Yang Z, Zeng H, Zhang W, Xu B, Hu W. Explainable cross-task adaptive transfer learning for motor imagery EEG classification. J Neural Eng 2023; 20:066021. [PMID: 37963394 DOI: 10.1088/1741-2552/ad0c61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective. In the field of motor imagery (MI) electroencephalography (EEG)-based brain-computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability in subject-specific data for the training of robust deep learning (DL) models. Although considerable progress has been made in the cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL remains largely unexplored.Approach. We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterwards, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradient-based post-hoc explainability analysis is conducted for the visualization of important temporal-spatial features.Main results. Extensive experiments are conducted on one large ME EEG High-Gamma dataset and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for OpenBMI and GIST respectively, which outperforms several state-of-the-art algorithms. In addition, the results of the explainability analysis further validate the correlation between ME and MI EEG data and the effectiveness of ME/MI cross-task adaptation.Significance. This paper confirms that the decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and is important in a practical sense.
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Affiliation(s)
- Minmin Miao
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou, People's Republic of China
| | - Zhong Yang
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
| | - Hong Zeng
- School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Wenbin Zhang
- College of Computer and Information, Hohai University, Nanjing, People's Republic of China
| | - Baoguo Xu
- School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China
| | - Wenjun Hu
- School of Information Engineering, Huzhou University, Huzhou, People's Republic of China
- Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou, People's Republic of China
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16
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Mustile M, Kourtis D, Edwards MG, Ladouce S, Volpe D, Pilleri M, Pelosin E, Learmonth G, Donaldson DI, Ietswaart M. Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles. Brain Commun 2023; 5:fcad326. [PMID: 38107501 PMCID: PMC10724048 DOI: 10.1093/braincomms/fcad326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 10/03/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023] Open
Abstract
The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.
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Affiliation(s)
- Magda Mustile
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
- The Psychological Sciences Research Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
| | - Dimitrios Kourtis
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
| | - Martin G Edwards
- The Psychological Sciences Research Institute, Université catholique de Louvain, 1348 Louvain-la-Neuve, Belgium
| | - Simon Ladouce
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - Daniele Volpe
- Fresco Parkinson Center, Villa Margherita, S. Stefano Riabilitazione, 36100 Vicenza, Italy
| | - Manuela Pilleri
- Fresco Parkinson Center, Villa Margherita, S. Stefano Riabilitazione, 36100 Vicenza, Italy
| | - Elisa Pelosin
- Ospedale Policlinico San Martino, IRCCS, 16132 Genova, Italy
| | - Gemma Learmonth
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
- School of Psychology & Neuroscience, University of Glasgow, Glasgow, G12 8QQ, UK
| | - David I Donaldson
- School of Psychology and Neuroscience, University of St Andrews, St. Andrews, KY16 9AJ, UK
| | - Magdalena Ietswaart
- Psychology, Faculty of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK
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17
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Tang Z, Wang H, Cui Z, Jin X, Zhang L, Peng Y, Xing B. An Upper-Limb Rehabilitation Exoskeleton System Controlled by MI Recognition Model With Deep Emphasized Informative Features in a VR Scene. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4390-4401. [PMID: 37910412 DOI: 10.1109/tnsre.2023.3329059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49% ± 3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82% ± 4.66% and 88.48% ± 5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.
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18
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Liang W, Jin J, Xu R, Wang X, Cichocki A. Variance characteristic preserving common spatial pattern for motor imagery BCI. Front Hum Neurosci 2023; 17:1243750. [PMID: 38021234 PMCID: PMC10666778 DOI: 10.3389/fnhum.2023.1243750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space. Methods This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly. Results The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm. Discussion The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
- Shenzhen Research Institute of East China University of Science and Technology, Shenzhen, China
| | - Ren Xu
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Toruń, Poland
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Wang J, Wang T, Liu H, Wang K, Moses K, Feng Z, Li P, Huang W. Flexible Electrodes for Brain-Computer Interface System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211012. [PMID: 37143288 DOI: 10.1002/adma.202211012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/27/2023] [Indexed: 05/06/2023]
Abstract
Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body, and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided.
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Affiliation(s)
- Junjie Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Tengjiao Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Haoyan Liu
- Department of Computer Science & Computer Engineering (CSCE), University of Arkansas, Fayetteville, AR, 72701, USA
| | - Kun Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Kumi Moses
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Zhuoya Feng
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Peng Li
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
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20
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Sun H, Jin J, Daly I, Huang Y, Zhao X, Wang X, Cichocki A. Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. J Neurosci Methods 2023; 399:109969. [PMID: 37683772 DOI: 10.1016/j.jneumeth.2023.109969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Shenzhen Research Institute of East China University of Science and Technology, Shen Zhen 518063, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Yitao Huang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xueqing Zhao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- RIKEN Brain Science Institute, Wako 351-0198, Japan; Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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21
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Zhang M, Huang J, Ni S. Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features. Front Neurosci 2023; 17:1270785. [PMID: 38027473 PMCID: PMC10643198 DOI: 10.3389/fnins.2023.1270785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb. Methods This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function. Results The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP. Discussion The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.
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Affiliation(s)
- Meng Zhang
- School of Mechanical and Power Engineering, Nanjing Tech University Nanjing, Nanjing, China
- Research Institute, NeuralEcho Technology Co., Ltd., Beijing, China
| | - Jinfeng Huang
- Faculty of Human Sciences, University of Tsukuba, Ibaraki, Japan
- Research Institute, NeuralEcho Technology Co., Ltd., Beijing, China
| | - Shoudong Ni
- School of Mechanical and Power Engineering, Nanjing Tech University Nanjing, Nanjing, China
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22
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Liang W, Jin J, Daly I, Sun H, Wang X, Cichocki A. Novel channel selection model based on graph convolutional network for motor imagery. Cogn Neurodyn 2023; 17:1283-1296. [PMID: 37786654 PMCID: PMC10542066 DOI: 10.1007/s11571-022-09892-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/03/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.
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Affiliation(s)
- Wei Liang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
- Shenzhen Research Institute of East China University of Technology, Shenzhen, 518063 China
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237 China
| | - Andrzej Cichocki
- Skolkovo Institute of Science and Technology, Moscow, Russia 143026
- Systems Research Institute of Polish Academy of Science, Warsaw, Poland
- Department of Informatics, Nicolaus Copernicus University, Torun, Poland
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Nunes JD, Vourvopoulos A, Blanco-Mora DA, Jorge C, Fernandes JC, Bermudez i Badia S, Figueiredo P. Brain activation by a VR-based motor imagery and observation task: An fMRI study. PLoS One 2023; 18:e0291528. [PMID: 37756271 PMCID: PMC10529559 DOI: 10.1371/journal.pone.0291528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 08/07/2023] [Indexed: 09/29/2023] Open
Abstract
Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.
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Affiliation(s)
- João D. Nunes
- INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, and Faculty of Engineering, University of Porto, Porto, Portugal
| | - Athanasios Vourvopoulos
- Institute for Systems and Robotics - Lisboa, and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Diego Andrés Blanco-Mora
- Faculdade de Ciências Exatas e da Engenharia, N-LINCS Madeira — ARDITI, Universidade da Madeira, Funchal, Portugal
| | - Carolina Jorge
- Faculdade de Ciências Exatas e da Engenharia, N-LINCS Madeira — ARDITI, Universidade da Madeira, Funchal, Portugal
| | - Jean-Claude Fernandes
- Central Hospital of Funchal, Physical Medicine and Rehabilitation Service, Funchal, Portugal
| | - Sergi Bermudez i Badia
- Faculdade de Ciências Exatas e da Engenharia, N-LINCS Madeira — ARDITI, Universidade da Madeira, Funchal, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics - Lisboa, and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
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Langiulli N, Calbi M, Sbravatti V, Umiltà MA, Gallese V. The effect of Surround sound on embodiment and sense of presence in cinematic experience: a behavioral and HD-EEG study. Front Neurosci 2023; 17:1222472. [PMID: 37746143 PMCID: PMC10513788 DOI: 10.3389/fnins.2023.1222472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 08/14/2023] [Indexed: 09/26/2023] Open
Abstract
Although many studies have investigated spectators' cinematic experience, only a few of them explored the neurophysiological correlates of the sense of presence evoked by the spatial characteristics of audio delivery devices. Nevertheless, nowadays both the industrial and the consumer markets have been saturated by some forms of spatial audio format that enrich the audio-visual cinematic experience, reducing the gap between the real and the digitally mediated world. The increase in the immersive capabilities corresponds to the instauration of both the sense of presence and the psychological sense of being in the virtual environment and also embodied simulation mechanisms. While it is well-known that these mechanisms can be activated in the real world, it is hypothesized that they may be elicited even in a virtual acoustic spatial environment and could be modulated by the acoustic spatialization cues reproduced by sound systems. Hence, the present study aims to investigate the neural basis of the sense of presence evoked by different forms of mediation by testing different acoustic space sound delivery (Presentation modes: Monophonic, Stereo, and Surround). To these aims, a behavioral investigation and a high-density electroencephalographic (HD-EEG) study have been developed. A large set of ecological and heterogeneous stimuli extracted from feature films were used. Furthermore, participants were selected following the generalized listener selection procedure. We found a significantly higher event-related desynchronization (ERD) in the Surround Presentation mode when compared to the Monophonic Presentation mode both in Alpha and Low-Beta centro-parietal clusters. We discuss this result as an index of embodied simulation mechanisms that could be considered as a possible neurophysiological correlation of the instauration of the sense of presence.
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Affiliation(s)
- Nunzio Langiulli
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marta Calbi
- Department of Philosophy “Piero Martinetti”, State University of Milan, Milan, Italy
| | - Valerio Sbravatti
- Department of History, Anthropology, Religions, Arts and Performing Arts, Sapienza University of Rome, Rome, Italy
| | - Maria Alessandra Umiltà
- Department of Food and Drug, University of Parma, Parma, Italy
- Italian Academy for Advanced Studies in America at Columbia University, New York, NY, United States
| | - Vittorio Gallese
- Unit of Neuroscience, Department of Medicine and Surgery, University of Parma, Parma, Italy
- Italian Academy for Advanced Studies in America at Columbia University, New York, NY, United States
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Misono S, Xu J, Oh J, Sombrio A, Stockness A, Mahnan A, Konczak J. Atypical Activation of Laryngeal Somatosensory-Motor Cortex During Vocalization in People With Unexplained Chronic Cough. JAMA Otolaryngol Head Neck Surg 2023; 149:820-827. [PMID: 37471077 PMCID: PMC10360007 DOI: 10.1001/jamaoto.2023.1757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 05/23/2023] [Indexed: 07/21/2023]
Abstract
Importance Unexplained chronic cough is common and has substantial negative quality-of-life implications, yet its causes are not well understood. A better understanding of how peripheral and central neural processes contribute to chronic cough is essential for treatment design. Objective To determine if people with chronic cough exhibit signs of abnormal neural processing over laryngeal sensorimotor cortex during voluntary laryngeal motor activity such as vocalization. Design, Setting, and Participants This was a cross-sectional study of a convenience sample of participants with chronic cough and healthy participants. Testing was performed in an acoustically and electromagnetically shielded chamber. In a single visit, electroencephalographic (EEG) signals were recorded from participants with chronic cough and healthy participants during voice production. The chronic cough group participants presented with unexplained cough of 8 weeks or longer duration with prior medical evaluation including negative results of chest imaging. None of the participants had a history of any neurologic disease known to impair vocalization or swallowing. Data collection for the healthy control group occurred from February 2 to June 28, 2018, and for the chronic cough group, from November 22, 2021, to June 21, 2022. Data analysis was performed from May 1 to October 30, 2022. Exposure Participants with or without chronic cough. Main Outcome Measures Event-related spectral perturbation over the laryngeal area of somatosensory-motor cortex from 0 to 30 Hz (ie, θ, α, and β bands) and event-related coherence as a measure of synchronous activity between somatosensory and motor cortical regions. Results The chronic cough group comprised 13 participants with chronic cough (mean [SD] age, 63.5 [7.8] years; 9 women and 4 men) and the control group, 10 healthy age-matched individuals (mean [SD] age, 60.3 [13.9] years; 6 women and 4 men). In the chronic cough group, the typical movement-related desynchronization over somatosensory-motor cortex during vocalization was significantly reduced across θ, α, and β frequency bands when compared with the control group. Conclusions and Relevance This cross-sectional study found that the typical movement-related suppression of brain oscillatory activity during vocalization is weak or absent in people with chronic cough. Thus, chronic cough affects sensorimotor cortical activity during the asymptomatic voluntary activation of laryngeal muscles.
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Affiliation(s)
- Stephanie Misono
- Department of Otolaryngology, Head and Neck Surgery, University of Minnesota, Minneapolis
| | - Jiapeng Xu
- Human Sensorimotor Control Laboratory, School of Kinesiology, University of Minnesota, Minneapolis
| | - Jinseok Oh
- Human Sensorimotor Control Laboratory, School of Kinesiology, University of Minnesota, Minneapolis
- Center for Clinical Movement Science, University of Minnesota, Minneapolis
- Department of Behavioral Pediatrics, Children’s Hospital Los Angeles, Los Angeles, California
| | - Anna Sombrio
- Department of Otolaryngology, Head and Neck Surgery, University of Minnesota, Minneapolis
| | - Ali Stockness
- Department of Otolaryngology, Head and Neck Surgery, University of Minnesota, Minneapolis
| | - Arash Mahnan
- Reality Labs Health and Safety UXR, Meta, Redmond, Washington
| | - Jürgen Konczak
- Human Sensorimotor Control Laboratory, School of Kinesiology, University of Minnesota, Minneapolis
- Center for Clinical Movement Science, University of Minnesota, Minneapolis
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Stier C, Braun C, Focke NK. Adult lifespan trajectories of neuromagnetic signals and interrelations with cortical thickness. Neuroimage 2023; 278:120275. [PMID: 37451375 PMCID: PMC10443236 DOI: 10.1016/j.neuroimage.2023.120275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Oscillatory power and phase synchronization map neuronal dynamics and are commonly studied to differentiate the healthy and diseased brain. Yet, little is known about the course and spatial variability of these features from early adulthood into old age. Leveraging magnetoencephalography (MEG) resting-state data in a cross-sectional adult sample (n = 350), we probed lifespan differences (18-88 years) in connectivity and power and interaction effects with sex. Building upon recent attempts to link brain structure and function, we tested the spatial correspondence between age effects on cortical thickness and those on functional networks. We further probed a direct structure-function relationship at the level of the study sample. We found MEG frequency-specific patterns with age and divergence between sexes in low frequencies. Connectivity and power exhibited distinct linear trajectories or turning points at midlife that might reflect different physiological processes. In the delta and beta bands, these age effects corresponded to those on cortical thickness, pointing to co-variation between the modalities across the lifespan. Structure-function coupling was frequency-dependent and observed in unimodal or multimodal regions. Altogether, we provide a comprehensive overview of the topographic functional profile of adulthood that can form a basis for neurocognitive and clinical investigations. This study further sheds new light on how the brain's structural architecture relates to fast oscillatory activity.
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Affiliation(s)
- Christina Stier
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany.
| | - Christoph Braun
- MEG-Center, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Niels K Focke
- Clinic of Neurology, University Medical Center Göttingen, Göttingen, Germany
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Ma X, Chen W, Pei Z, Liu J, Huang B, Chen J. A Temporal Dependency Learning CNN With Attention Mechanism for MI-EEG Decoding. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3188-3200. [PMID: 37498754 DOI: 10.1109/tnsre.2023.3299355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.
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Gäumann S, Aksöz EA, Behrendt F, Wandel J, Cappelletti L, Krug A, Mörder D, Bill A, Parmar K, Gerth HU, Bonati LH, Schuster-Amft C. The challenge of measuring physiological parameters during motor imagery engagement in patients after a stroke. Front Neurosci 2023; 17:1225440. [PMID: 37583419 PMCID: PMC10423937 DOI: 10.3389/fnins.2023.1225440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 07/11/2023] [Indexed: 08/17/2023] Open
Abstract
Introduction It is suggested that eye movement recordings could be used as an objective evaluation method of motor imagery (MI) engagement. Our investigation aimed to evaluate MI engagement in patients after stroke (PaS) compared with physical execution (PE) of a clinically relevant unilateral upper limb movement task of the patients' affected body side. Methods In total, 21 PaS fulfilled the MI ability evaluation [Kinaesthetic and Visual Imagery Questionnaire (KVIQ-10), body rotation task (BRT), and mental chronometry task (MC)]. During the experiment, PaS moved a cup to distinct fields while wearing smart eyeglasses (SE) with electrooculography electrodes integrated into the nose pads and electrodes for conventional electrooculography (EOG). To verify MI engagement, heart rate (HR) and oxygen saturation (SpO2) were recorded, simultaneously with electroencephalography (EEG). Eye movements were recorded during MI, PE, and rest in two measurement sessions to compare the SE performance between conditions and SE's psychometric properties. Results MI and PE correlation of SE signals varied between r = 0.12 and r = 0.76. Validity (cross-correlation with EOG signals) was calculated for MI (r = 0.53) and PE (r = 0.57). The SE showed moderate test-retest reliability (intraclass correlation coefficient) with r = 0.51 (95% CI 0.26-0.80) for MI and with r = 0.53 (95% CI 0.29 - 0.76) for PE. Event-related desynchronization and event-related synchronization changes of EEG showed a large variability. HR and SpO2 recordings showed similar values during MI and PE. The linear mixed model to examine HR and SpO2 between conditions (MI, PE, rest) revealed a significant difference in HR between rest and MI, and between rest and PE but not for SpO2. A Pearson correlation between MI ability assessments (KVIQ, BRT, MC) and physiological parameters showed no association between MI ability and HR and SpO2. Conclusion The objective assessment of MI engagement in PaS remains challenging in clinical settings. However, HR was confirmed as a reliable parameter to assess MI engagement in PaS. Eye movements measured with the SE during MI did not resemble those during PE, which is presumably due to the demanding task. A re-evaluation with task adaptation is suggested.
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Affiliation(s)
- Szabina Gäumann
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
| | - Efe Anil Aksöz
- School of Engineering and Information Technology, Bern University of Applied Sciences, Biel, Switzerland
| | - Frank Behrendt
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
- School of Engineering and Information Technology, Bern University of Applied Sciences, Biel, Switzerland
| | - Jasmin Wandel
- Institute for Optimisation and Data Analysis, Bern University of Applied Sciences, Burgdorf, Switzerland
| | - Letizia Cappelletti
- Department of Health Professions, Bern University of Applied Science, Bern, Switzerland
| | - Annika Krug
- Institute for Physiotherapy, School of Health Professions, Zurich University of Applied Sciences, Winterthur, Switzerland
| | - Daniel Mörder
- Department of Sport Science, Faculty of Humanities, University of Konstanz, Konstanz, Germany
| | - Annika Bill
- Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Katrin Parmar
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
| | - Hans Ulrich Gerth
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
- Department of Medicine, University Hospital Münster, Münster, Germany
| | - Leo H. Bonati
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Department of Clinical Research, University of Basel, Basel, Switzerland
| | - Corina Schuster-Amft
- Department of Research, Reha Rheinfelden, Rheinfelden, Switzerland
- School of Engineering and Information Technology, Bern University of Applied Sciences, Biel, Switzerland
- Department of Sport, Physical Activity, and Health, University of Basel, Basel, Switzerland
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Yakovlev L, Syrov N, Kaplan A. Investigating the influence of functional electrical stimulation on motor imagery related μ-rhythm suppression. Front Neurosci 2023; 17:1202951. [PMID: 37492407 PMCID: PMC10365101 DOI: 10.3389/fnins.2023.1202951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023] Open
Abstract
Background Motor Imagery (MI) is a well-known cognitive technique that utilizes the same neural circuits as voluntary movements. Therefore, MI practice is widely used in sport training and post-stroke rehabilitation. The suppression of the μ-rhythm in electroencephalogram (EEG) is a conventional marker of sensorimotor cortical activation during motor imagery. However, the role of somatosensory afferentation in mental imagery processes is not yet clear. In this study, we investigated the impact of functional electrical stimulation (FES) on μ-rhythm suppression during motor imagery. Methods Thirteen healthy experienced participants were asked to imagine their right hand grasping, while a 30-channel EEG was recorded. FES was used to influence sensorimotor activation during motor imagery of the same hand. Results We evaluated cortical activation by estimating the μ-rhythm suppression index, which was assessed in three experimental conditions: MI, MI + FES, and FES. Our findings shows that motor imagery enhanced by FES leads to a more prominent μ-rhythm suppression. Obtained results suggest a direct effect of peripheral electrical stimulation on cortical activation, especially when combined with motor imagery. Conclusion This research sheds light on the potential benefits of integrating FES into motor imagery-based interventions to enhance cortical activation and holds promise for applications in neurorehabilitation.
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Affiliation(s)
- Lev Yakovlev
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Nikolay Syrov
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Alexander Kaplan
- Vladimir Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Laboratory for Neurophysiology and Neuro-Computer Interfaces, Lomonosov Moscow State University, Moscow, Russia
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Lakshminarayanan K, Shah R, Daulat SR, Moodley V, Yao Y, Madathil D. The effect of combining action observation in virtual reality with kinesthetic motor imagery on cortical activity. Front Neurosci 2023; 17:1201865. [PMID: 37383098 PMCID: PMC10299830 DOI: 10.3389/fnins.2023.1201865] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/25/2023] [Indexed: 06/30/2023] Open
Abstract
Introduction In the past, various techniques have been used to improve motor imagery (MI), such as immersive virtual-reality (VR) and kinesthetic rehearsal. While electroencephalography (EEG) has been used to study the differences in brain activity between VR-based action observation and kinesthetic motor imagery (KMI), there has been no investigation into their combined effect. Prior research has demonstrated that VR-based action observation can enhance MI by providing both visual information and embodiment, which is the perception of oneself as part of the observed entity. Additionally, KMI has been found to produce similar brain activity to physically performing a task. Therefore, we hypothesized that utilizing VR to offer an immersive visual scenario for action observation while participants performed kinesthetic motor imagery would significantly improve cortical activity related to MI. Methods In this study, 15 participants (9 male, 6 female) performed kinesthetic motor imagery of three hand tasks (drinking, wrist flexion-extension, and grabbing) both with and without VR-based action observation. Results Our results indicate that combining VR-based action observation with KMI enhances brain rhythmic patterns and provides better task differentiation compared to KMI without action observation. Discussion These findings suggest that using VR-based action observation alongside kinesthetic motor imagery can improve motor imagery performance.
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Affiliation(s)
- Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Rakshit Shah
- Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH, United States
| | - Sohail R. Daulat
- Department of Physiology, University of Arizona College of Medicine – Tucson, Tucson, AZ, United States
| | - Viashen Moodley
- Arizona Center for Hand to Shoulder Surgery, Phoenix, AZ, United States
| | - Yifei Yao
- Soft Tissue Biomechanics Laboratory, School of Biomedical Engineering, Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Deepa Madathil
- Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Sonipat, Haryana, India
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Ramu V, Lakshminarayanan K. Enhanced motor imagery of digits within the same hand via vibrotactile stimulation. Front Neurosci 2023; 17:1152563. [PMID: 37360173 PMCID: PMC10289883 DOI: 10.3389/fnins.2023.1152563] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/26/2023] [Indexed: 06/28/2023] Open
Abstract
Purpose The aim of the present study is to evaluate the effect of vibrotactile stimulation prior to repeated complex motor imagery of finger movements using the non-dominant hand on motor imagery (MI) performance. Methods Ten healthy right-handed adults (4 females and 6 males) participated in the study. The subjects performed motor imagery tasks with and without a brief vibrotactile sensory stimulation prior to performing motor imagery using either their left-hand index, middle, or thumb digits. Mu- and beta-band event-related desynchronization (ERD) at the sensorimotor cortex and an artificial neural network-based digit classification was evaluated. Results The ERD and digit discrimination results from our study showed that ERD was significantly different between the vibration conditions for the index, middle, and thumb. It was also found that digit classification accuracy with-vibration (mean ± SD = 66.31 ± 3.79%) was significantly higher than without-vibration (mean ± SD = 62.68 ± 6.58%). Conclusion The results showed that a brief vibration was more effective at improving MI-based brain-computer interface classification of digits within a single limb through increased ERD compared to performing MI without vibrotactile stimulation.
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Gharesi N, Luneau L, Kalaska JF, Baillet S. Evaluation of abstract rule-based associations in the human premotor cortex during passive observation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543581. [PMID: 37333191 PMCID: PMC10274620 DOI: 10.1101/2023.06.06.543581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Decision-making often manifests in behavior, typically yielding overt motor actions. This complex process requires the registration of sensory information with one's internal representation of the current context, before a categorical judgment of the most appropriate motor behavior can be issued. The construct concept of embodied decision-making encapsulates this sequence of complex processes, whereby behaviorally salient information from the environment is represented in an abstracted space of potential motor actions rather than only in an abstract cognitive "decision" space. Theoretical foundations and some empirical evidence account for support the involvement of premotor cortical circuits in embodied cognitive functions. Animal models show that premotor circuits participate in the registration and evaluation of actions performed by peers in social situations, that is, prior to controlling one's voluntary movements guided by arbitrary stimulus-response rules. However, such evidence from human data is currently limited. Here we used time-resolved magnetoencephalography imaging to characterize activations of the premotor cortex as human participants observed arbitrary, non-biological visual stimuli that either respected or violated a simple stimulus-response association rule. The participants had learned this rule previously, either actively, by performing a motor task (active learning), or passively, by observing a computer perform the same task (passive learning). We discovered that the human premotor cortex is activated during the passive observation of the correct execution of a sequence of events according to a rule learned previously. Premotor activation also differs when the subjects observe incorrect stimulus sequences. These premotor effects are present even when the observed events are of a non-motor, abstract nature, and even when the stimulus-response association rule was learned via passive observations of a computer agent performing the task, without requiring overt motor actions from the human participant. We found evidence of these phenomena by tracking cortical beta-band signaling in temporal alignment with the observation of task events and behavior. We conclude that premotor cortical circuits that are typically engaged during voluntary motor behavior are also involved in the interpretation of events of a non-ecological, unfamiliar nature but related to a learned abstract rule. As such, the present study provides the first evidence of neurophysiological processes of embodied decision-making in human premotor circuits when the observed events do not involve motor actions of a third party.
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Affiliation(s)
- Niloofar Gharesi
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Lucie Luneau
- Groupe de recherche sur la signalisation neuronale et la circuiterie, Département de Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - John F Kalaska
- Groupe de recherche sur la signalisation neuronale et la circuiterie, Département de Neurosciences, Université de Montréal, Montréal, QC, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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Kurkin S, Gordleeva S, Savosenkov A, Grigorev N, Smirnov N, Grubov VV, Udoratina A, Maksimenko V, Kazantsev V, Hramov AE. Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery. SENSORS (BASEL, SWITZERLAND) 2023; 23:4661. [PMID: 37430576 DOI: 10.3390/s23104661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 07/12/2023]
Abstract
Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.
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Affiliation(s)
- Semen Kurkin
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Susanna Gordleeva
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Andrey Savosenkov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Grigorev
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Nikita Smirnov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Vadim V Grubov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
| | - Anna Udoratina
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Vladimir Maksimenko
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Victor Kazantsev
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
| | - Alexander E Hramov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
- Neurodynamics and Cognitive Technology Laboratory, Lobachevsky State University of Nizhny Novgorod, 603105 Nizhniy Novgorod, Russia
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Lakshminarayanan K, Shah R, Daulat SR, Moodley V, Yao Y, Sengupta P, Ramu V, Madathil D. Evaluation of EEG Oscillatory Patterns and Classification of Compound Limb Tactile Imagery. Brain Sci 2023; 13:brainsci13040656. [PMID: 37190621 DOI: 10.3390/brainsci13040656] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain-computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices.
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Affiliation(s)
- Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Rakshit Shah
- Department of Chemical and Biomedical Engineering, Cleveland State University, Cleveland, OH 44115, USA
| | - Sohail R Daulat
- Department of Physiology, University of Arizona College of Medicine, Tucson, AZ 85724, USA
| | - Viashen Moodley
- Arizona Center for Hand to Shoulder Surgery, Phoenix, AZ 85004, USA
| | - Yifei Yao
- Soft Tissue Biomechanics Laboratory, Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Vadivelan Ramu
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Deepa Madathil
- Jindal Institute of Behavioral Sciences, O. P. Jindal Global University, Sonipat 131001, Haryana, India
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Guillot A, Daligault S, Schwartz D, Di Rienzo F. Timing-specific patterns of cerebral activations during motor imagery: A case study of the expert brain signature. Brain Cogn 2023; 167:105971. [PMID: 37011436 DOI: 10.1016/j.bandc.2023.105971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/14/2023] [Accepted: 03/16/2023] [Indexed: 04/03/2023]
Abstract
Brain activations elicited during motor imagery (MI) in experts are typically reduced compared to novices, which is interpreted as a neurophysiological correlate of increased neural efficiency. However, the modulatory effects of MI speed on expertise-related differences in brain activation remains largely unknown. In the present pilot study, we compared the magnetoencephalographic (MEG) correlates of MI in an Olympic medallist and an amateur athlete under conditions of slow, real-time and fast MI. Data revealed event-related changes in the time course of alpha (8-12 Hz) power of MEG oscillations, for all timing conditions. We found that slow MI was associated with a corollary increase in neural synchronization, in both participants. Sensor-level and source-level analyses however disclosed differences between the two expertise levels. The Olympic medallist achieved greater activation of cortical sensorimotor networks than the amateur athlete, particularly during fast MI. Fast MI elicited the strongest event-related desynchronization of alpha oscillations, which was generated from cortical sensorimotor sources in the Olympic medallist, but not in the amateur athlete. Taken together, data suggest that fast MI is a particularly demanding form of motor cognition, putting a specific emphasis on cortical sensorimotor networks to achieve the formation of accurate motor representations under demanding timing constraints.
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Deep stacked least square support matrix machine with adaptive multi-layer transfer for EEG classification. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Gwon D, Won K, Song M, Nam CS, Jun SC, Ahn M. Review of public motor imagery and execution datasets in brain-computer interfaces. Front Hum Neurosci 2023; 17:1134869. [PMID: 37063105 PMCID: PMC10101208 DOI: 10.3389/fnhum.2023.1134869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 03/10/2023] [Indexed: 04/18/2023] Open
Abstract
The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.
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Affiliation(s)
- Daeun Gwon
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
| | - Kyungho Won
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Minseok Song
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
| | - Chang S. Nam
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States
- Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, Republic of Korea
| | - Sung Chan Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
- AI Graudate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Minkyu Ahn
- Department of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
- School of Computer Science and Electrical Engineering, Handong Global University, Pohang, Republic of Korea
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Farabbi A, Figueiredo P, Ghiringhelli F, Mainardi L, Sanches JM, Moreno P, Santos-Victor J, Vourvopoulos A. Investigating the impact of visual perspective in a motor imagery-based brain-robot interaction: A pilot study with healthy participants. FRONTIERS IN NEUROERGONOMICS 2023; 4:1080794. [PMID: 38234500 PMCID: PMC10790830 DOI: 10.3389/fnrgo.2023.1080794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 03/08/2023] [Indexed: 01/19/2024]
Abstract
Introduction Motor Imagery (MI)-based Brain Computer Interfaces (BCI) have raised gained attention for their use in rehabilitation therapies since they allow controlling an external device by using brain activity, in this way promoting brain plasticity mechanisms that could lead to motor recovery. Specifically, rehabilitation robotics can provide precision and consistency for movement exercises, while embodied robotics could provide sensory feedback that can help patients improve their motor skills and coordination. However, it is still not clear whether different types of visual feedback may affect the elicited brain response and hence the effectiveness of MI-BCI for rehabilitation. Methods In this paper, we compare two visual feedback strategies based on controlling the movement of robotic arms through a MI-BCI system: 1) first-person perspective, with visual information that the user receives when they view the robot arms from their own perspective; and 2) third-person perspective, whereby the subjects observe the robot from an external perspective. We studied 10 healthy subjects over three consecutive sessions. The electroencephalographic (EEG) signals were recorded and evaluated in terms of the power of the sensorimotor rhythms, as well as their lateralization, and spatial distribution. Results Our results show that both feedback perspectives can elicit motor-related brain responses, but without any significant differences between them. Moreover, the evoked responses remained consistent across all sessions, showing no significant differences between the first and the last session. Discussion Overall, these results suggest that the type of perspective may not influence the brain responses during a MI- BCI task based on a robotic feedback, although, due to the limited sample size, more evidence is required. Finally, this study resulted into the production of 180 labeled MI EEG datasets, publicly available for research purposes.
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Affiliation(s)
- Andrea Farabbi
- B3Lab, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
| | - Patricia Figueiredo
- Institute for Systems and Robotics-Lisboa, Instituto Superior Tecnico, Lisbon, Portugal
| | - Fabiola Ghiringhelli
- B3Lab, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- B3Lab, Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
| | - Joao Miguel Sanches
- Institute for Systems and Robotics-Lisboa, Instituto Superior Tecnico, Lisbon, Portugal
| | - Plinio Moreno
- Institute for Systems and Robotics-Lisboa, Instituto Superior Tecnico, Lisbon, Portugal
| | - Jose Santos-Victor
- Institute for Systems and Robotics-Lisboa, Instituto Superior Tecnico, Lisbon, Portugal
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Saibene A, Caglioni M, Corchs S, Gasparini F. EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2798. [PMID: 36905004 PMCID: PMC10007053 DOI: 10.3390/s23052798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.
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Affiliation(s)
- Aurora Saibene
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
| | - Mirko Caglioni
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Silvia Corchs
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
- Department of Theoretical and Applied Sciences, University of Insubria, Via J. H. Dunant 3, 21100 Varese, Italy
| | - Francesca Gasparini
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
- NeuroMI, Milan Center for Neuroscience, Piazza dell’Ateneo Nuovo 1, 20126 Milano, Italy
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40
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De Sanctis P, Wagner J, Molholm S, Foxe JJ, Blumen HM, Horsthuis DJ. Neural signature of mobility-related everyday function in older adults at-risk of cognitive impairment. Neurobiol Aging 2023; 122:1-11. [PMID: 36463848 PMCID: PMC10281759 DOI: 10.1016/j.neurobiolaging.2022.11.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 10/27/2022] [Accepted: 11/03/2022] [Indexed: 11/10/2022]
Abstract
Assessment of everyday activities is central to the diagnosis of dementia. Yet, little is known about brain processes associated with everyday functional limitations, particularly during early stages of cognitive decline. Twenty-six older adults (mean = 74.9 y) were stratified by risk using the Montreal Cognitive Assessment battery (MoCA, range: 0- 30) to classify individuals as higher (22-26) and lower risk (27+) of cognitive impairment. We investigated everyday function using a gait task designed to destabilize posture and applied Mobile Brain/Body Imaging. We predicted that participants would increase step width to gain stability, yet the underlying neural signatures would be different for lower versus higher risk individuals. Step width and fronto-parietal activation increased during visually perturbed input. Frontomedial theta increased in higher risk individuals during perturbed and unperturbed inputs. Left sensorimotor beta decreased in lower risk individuals during visually perturbed input. Modulations in theta and beta power were associated with MoCA scores. Our findings suggest that older adults at-risk of cognitive impairment can be characterized by a unique neural signature of everyday function.
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Affiliation(s)
- Pierfilippo De Sanctis
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Bronx, NY, USA.
| | - Johanna Wagner
- Computational Neuroscience, Institute for Neural Computation, University of California, San Diego, La Jolla, CA, USA
| | - Sophie Molholm
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; The Dominick P. Purpura Department of Neuroscience, Rose F. Kennedy Intellectual and Developmental Disabilities Research Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John J Foxe
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA; The Cognitive Neurophysiology Laboratory, The Del Monte Institute for Neuroscience, Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Helena M Blumen
- Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Medicine (Geriatrics), Albert Einstein College of Medicine, Bronx, NY, USA
| | - Douwe J Horsthuis
- The Cognitive Neurophysiology Laboratory, Department of Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
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West TO, Duchet B, Farmer SF, Friston KJ, Cagnan H. When do bursts matter in the primary motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states. Prog Neurobiol 2023; 221:102397. [PMID: 36565984 PMCID: PMC7614511 DOI: 10.1016/j.pneurobio.2022.102397] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/04/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Brain activity exhibits significant temporal structure that is not well captured in the power spectrum. Recently, attention has shifted to characterising the properties of intermittencies in rhythmic neural activity (i.e. bursts), yet the mechanisms that regulate them are unknown. Here, we present evidence from electrocorticography recordings made over the motor cortex to show that the statistics of bursts, such as duration or amplitude, in the beta frequency (14-30 Hz) band, significantly aid the classification of motor states such as rest, movement preparation, execution, and imagery. These features reflect nonlinearities not detectable in the power spectrum, with states increasing in nonlinearity from movement execution to preparation to rest. Further, we show using a computational model of the cortical microcircuit, constrained to account for burst features, that modulations of laminar specific inhibitory interneurons are responsible for the temporal organisation of activity. Finally, we show that the temporal characteristics of spontaneous activity can be used to infer the balance of cortical integration between incoming sensory information and endogenous activity. Critically, we contribute to the understanding of how transient brain rhythms may underwrite cortical processing, which in turn, could inform novel approaches for brain state classification, and modulation with novel brain-computer interfaces.
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Affiliation(s)
- Timothy O West
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK.
| | - Benoit Duchet
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK
| | - Simon F Farmer
- Department of Neurology, National Hospital for Neurology & Neurosurgery, Queen Square, London WC1N 3BG, UK; Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Hayriye Cagnan
- Medical Research Council Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Mansfield Road, Oxford OX1 3TH, UK; Wellcome Centre for Human Neuroimaging, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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Milanés-Hermosilla D, Trujillo-Codorniú R, Lamar-Carbonell S, Sagaró-Zamora R, Tamayo-Pacheco JJ, Villarejo-Mayor JJ, Delisle-Rodriguez D. Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:703. [PMID: 36679501 PMCID: PMC9862912 DOI: 10.3390/s23020703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
The development of Brain-Computer Interfaces based on Motor Imagery (MI) tasks is a relevant research topic worldwide. The design of accurate and reliable BCI systems remains a challenge, mainly in terms of increasing performance and usability. Classifiers based on Bayesian Neural Networks are proposed in this work by using the variational inference, aiming to analyze the uncertainty during the MI prediction. An adaptive threshold scheme is proposed here for MI classification with a reject option, and its performance on both datasets 2a and 2b from BCI Competition IV is compared with other approaches based on thresholds. The results using subject-specific and non-subject-specific training strategies are encouraging. From the uncertainty analysis, considerations for reducing computational cost are proposed for future work.
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Affiliation(s)
| | - Rafael Trujillo-Codorniú
- Department of Automatic Engineering, University of Oriente, Santiago de Cuba 90500, Cuba
- Electronics, Communications and Computing Services Company for the Nickel Industry, Holguín 80100, Cuba
| | | | - Roberto Sagaró-Zamora
- Department of Mechanical Engineering, University of Oriente, Santiago de Cuba 90500, Cuba
| | | | - John Jairo Villarejo-Mayor
- Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianopolis 88040-900, SC, Brazil
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba 59280-000, RN, Brazil
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Syrov N, Yakovlev L, Miroshnikov A, Kaplan A. Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI. Front Hum Neurosci 2023; 17:1180056. [PMID: 37213933 PMCID: PMC10192585 DOI: 10.3389/fnhum.2023.1180056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/13/2023] [Indexed: 05/23/2023] Open
Abstract
Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain-computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation.
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Affiliation(s)
- Nikolay Syrov
- V. Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- *Correspondence: Nikolay Syrov,
| | - Lev Yakovlev
- V. Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Andrei Miroshnikov
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Alexander Kaplan
- V. Zelman Center for Neurobiology and Brain Rehabilitation, Skolkovo Institute of Science and Technology, Moscow, Russia
- Baltic Center for Neurotechnology and Artificial Intelligence, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
- Department of Human and Animal Physiology, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
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44
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The Effects of Subthreshold Vibratory Noise on Cortical Activity During Motor Imagery. Motor Control 2023:1-14. [PMID: 36801814 DOI: 10.1123/mc.2022-0061] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 12/04/2022] [Accepted: 01/08/2023] [Indexed: 02/19/2023]
Abstract
Previous studies have demonstrated that both visual and proprioceptive feedback play vital roles in mental practice of movements. Tactile sensation has been shown to improve with peripheral sensory stimulation via imperceptible vibratory noise by stimulating the sensorimotor cortex. With both proprioception and tactile sensation sharing the same population of posterior parietal neurons encoding within high-level spatial representations, the effect of imperceptible vibratory noise on motor imagery-based brain-computer interface is unknown. The objective of this study was to investigate the effects of this sensory stimulation via imperceptible vibratory noise applied to the index fingertip in improving motor imagery-based brain-computer interface performance. Fifteen healthy adults (nine males and six females) were studied. Each subject performed three motor imagery tasks, namely drinking, grabbing, and flexion-extension of the wrist, with and without sensory stimulation while being presented a rich immersive visual scenario through a virtual reality headset. Results showed that vibratory noise increased event-related desynchronization during motor imagery compared with no vibration. Furthermore, the task classification percentage was higher with vibration when the tasks were discriminated using a machine learning algorithm. In conclusion, subthreshold random frequency vibration affected motor imagery-related event-related desynchronization and improved task classification performance.
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Peter J, Ferraioli F, Mathew D, George S, Chan C, Alalade T, Salcedo SA, Saed S, Tatti E, Quartarone A, Ghilardi MF. Movement-related beta ERD and ERS abnormalities in neuropsychiatric disorders. Front Neurosci 2022; 16:1045715. [PMID: 36507340 PMCID: PMC9726921 DOI: 10.3389/fnins.2022.1045715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022] Open
Abstract
Movement-related oscillations in the beta range (from 13 to 30 Hz) have been observed over sensorimotor areas with power decrease (i.e., event-related desynchronization, ERD) during motor planning and execution followed by an increase (i.e., event-related synchronization, ERS) after the movement's end. These phenomena occur during active, passive, imaged, and observed movements. Several electrophysiology studies have used beta ERD and ERS as functional indices of sensorimotor integrity, primarily in diseases affecting the motor system. Recent literature also highlights other characteristics of beta ERD and ERS, implying their role in processes not strictly related to motor function. Here we review studies about movement-related ERD and ERS in diseases characterized by motor dysfunction, including Parkinson's disease, dystonia, stroke, amyotrophic lateral sclerosis, cerebral palsy, and multiple sclerosis. We also review changes of beta ERD and ERS reported in physiological aging, Alzheimer's disease, and schizophrenia, three conditions without overt motor symptoms. The review of these works shows that ERD and ERS abnormalities are present across the spectrum of the examined pathologies as well as development and aging. They further suggest that cognition and movement are tightly related processes that may share common mechanisms regulated by beta modulation. Future studies with a multimodal approach are warranted to understand not only the specific topographical dynamics of movement-related beta modulation but also the general meaning of beta frequency changes occurring in relation to movement and cognitive processes at large. Such an approach will provide the foundation to devise and implement novel therapeutic approaches to neuropsychiatric disorders.
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Affiliation(s)
- Jaime Peter
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Francesca Ferraioli
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Dave Mathew
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Shaina George
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Cameron Chan
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Tomisin Alalade
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Sheilla A. Salcedo
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Shannon Saed
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States
| | - Elisa Tatti
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States,*Correspondence: Elisa Tatti,
| | - Angelo Quartarone
- IRCCS Centro Neurolesi Bonino Pulejo-Piemonte, Messina, Italy,Angelo Quartarone,
| | - M. Felice Ghilardi
- Department of Molecular, Cellular and Biomedical Sciences, CUNY School of Medicine, New York, NY, United States,M. Felice Ghilardi,
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46
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Liu S, Zhang J, Wang A, Wu H, Zhao Q, Long J. Subject adaptation convolutional neural network for EEG-based motor imagery classification. J Neural Eng 2022; 19. [PMID: 36270467 DOI: 10.1088/1741-2552/ac9c94] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/21/2022] [Indexed: 01/11/2023]
Abstract
Objective.Deep transfer learning has been widely used to address the nonstationarity of electroencephalogram (EEG) data during motor imagery (MI) classification. However, previous deep learning approaches suffer from limited classification accuracy because the temporal and spatial features cannot be effectively extracted.Approach.Here, we propose a novel end-to-end deep subject adaptation convolutional neural network (SACNN) to handle the problem of EEG-based MI classification. Our proposed model jointly optimizes three modules, i.e. a feature extractor, a classifier, and a subject adapter. Specifically, the feature extractor simultaneously extracts the temporal and spatial features from the raw EEG data using a parallel multiscale convolution network. In addition, we design a subject adapter to reduce the feature distribution shift between the source and target subjects by using the maximum mean discrepancy. By minimizing the classification loss and the distribution discrepancy, the model is able to extract the temporal-spatial features to the prediction of a new subject.Main results.Extensive experiments are carried out on three EEG-based MI datasets, i.e. brain-computer interface (BCI) competition IV dataset IIb, BCI competition III dataset IVa, and BCI competition IV dataset I, and the average accuracy reaches to 86.42%, 81.71% and 79.35% on the three datasets respectively. Furthermore, the statistical analysis also indicates the significant performance improvement of SACNN.Significance.This paper reveals the importance of the temporal-spatial features on EEG-based MI classification task. Our proposed SACNN model can make fully use of the temporal-spatial information to achieve the purpose.
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Affiliation(s)
- Siwei Liu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Jia Zhang
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Andong Wang
- Tensor Learning Team, RIKEN AIP, Tokyo, Japan
| | - Hanrui Wu
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China
| | - Qibin Zhao
- Tensor Learning Team, RIKEN AIP, Tokyo, Japan
| | - Jinyi Long
- College of Information Science and Technology, Jinan University, Guangzhou 510632, People's Republic of China.,Guangdong Key Laboratory of Traditional Chinese Medicine Information Technology, Guangzhou 510632, People's Republic of China.,Pazhou Lab, Guangzhou 510335, People's Republic of China
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47
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Cao L, Wu H, Chen S, Dong Y, Zhu C, Jia J, Fan C. A Novel Deep Learning Method Based on an Overlapping Time Window Strategy for Brain-Computer Interface-Based Stroke Rehabilitation. Brain Sci 2022; 12:1502. [PMID: 36358428 PMCID: PMC9688819 DOI: 10.3390/brainsci12111502] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 09/22/2023] Open
Abstract
Globally, stroke is a leading cause of death and disability. The classification of motor intentions using brain activity is an important task in the rehabilitation of stroke patients using brain-computer interfaces (BCIs). This paper presents a new method for model training in EEG-based BCI rehabilitation by using overlapping time windows. For this aim, three different models, a convolutional neural network (CNN), graph isomorphism network (GIN), and long short-term memory (LSTM), are used for performing the classification task of motor attempt (MA). We conducted several experiments with different time window lengths, and the results showed that the deep learning approach based on overlapping time windows achieved improvements in classification accuracy, with the LSTM combined vote-counting strategy (VS) having achieved the highest average classification accuracy of 90.3% when the window size was 70. The results verified that the overlapping time window strategy is useful for increasing the efficiency of BCI rehabilitation.
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Affiliation(s)
- Lei Cao
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Hailiang Wu
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Shugeng Chen
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yilin Dong
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Changming Zhu
- Department of Artificial Intelligence, Shanghai Maritime University, Shanghai 201306, China
| | - Jie Jia
- Department of Rehabilitation Medicine, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chunjiang Fan
- Department of Rehabilitation Medicine, Wuxi Rehabilitation Hospital, Wuxi 214001, China
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48
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Altered functional connectivity: A possible reason for reduced performance during visual cognition involving scene incongruence and negative affect. IBRO Neurosci Rep 2022; 13:533-542. [DOI: 10.1016/j.ibneur.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 11/19/2022] [Indexed: 11/22/2022] Open
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49
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Li Z, Iramina K. Spatio-Temporal Neural Dynamics of Observing Non-Tool Manipulable Objects and Interactions. SENSORS (BASEL, SWITZERLAND) 2022; 22:7771. [PMID: 36298121 PMCID: PMC9611388 DOI: 10.3390/s22207771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 06/16/2023]
Abstract
Previous studies have reported that a series of sensory-motor-related cortical areas are affected when a healthy human is presented with images of tools. This phenomenon has been explained as familiar tools launching a memory-retrieval process to provide a basis for using the tools. Consequently, we postulated that this theory may also be applicable if images of tools were replaced with images of daily objects if they are graspable (i.e., manipulable). Therefore, we designed and ran experiments with human volunteers (participants) who were visually presented with images of three different daily objects and recorded their electroencephalography (EEG) synchronously. Additionally, images of these objects being grasped by human hands were presented to the participants. Dynamic functional connectivity between the visual cortex and all the other areas of the brain was estimated to find which of them were influenced by visual stimuli. Next, we compared our results with those of previous studies that investigated brain response when participants looked at tools and concluded that manipulable objects caused similar cerebral activity to tools. We also looked into mu rhythm and found that looking at a manipulable object did not elicit a similar activity to seeing the same object being grasped.
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Affiliation(s)
- Zhaoxuan Li
- Graduate School of Systems Life Sciences, Kyushu University, Fukuoka 8190395, Japan
| | - Keiji Iramina
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka 8190395, Japan
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50
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Zhang W, Wang Z, Wu D. Multi-Source Decentralized Transfer for Privacy-Preserving BCIs. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2710-2720. [PMID: 36112563 DOI: 10.1109/tnsre.2022.3207494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Transfer learning, which utilizes labeled source domains to facilitate the learning in a target model, is effective in alleviating high intra- and inter-subject variations in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Existing transfer learning approaches usually use the source subjects' EEG data directly, leading to privacy concerns. This paper considers a decentralized privacy-preserving transfer learning scenario: there are multiple source subjects, whose data and computations are kept local, and only the parameters or predictions of their pre-trained models can be accessed for privacy-protection; then, how to perform effective cross-subject transfer for a new subject with unlabeled EEG trials? We propose an offline unsupervised multi-source decentralized transfer (MSDT) approach, which first generates a pre-trained model from each source subject, and then performs decentralized transfer using the source model parameters (in gray-box settings) or predictions (in black-box settings). Experiments on two datasets from two BCI paradigms, motor imagery and affective BCI, demonstrated that MSDT outperformed several existing approaches, which do not consider privacy-protection at all. In other words, MSDT achieved both high privacy-protection and better classification performance.
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