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Wang A, Tian X, Jiang D, Yang C, Xu Q, Zhang Y, Zhao S, Zhang X, Jing J, Wei N, Wu Y, Lv W, Yang B, Zang D, Wang Y, Zhang Y, Wang Y, Meng X. Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial. MED 2024; 5:559-569.e4. [PMID: 38642555 DOI: 10.1016/j.medj.2024.02.014] [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: 12/08/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Upper limb motor dysfunction is a major problem in the rehabilitation of patients with stroke. Brain-computer interface (BCI) is a kind of communication system that converts the "ideas" in the brain into instructions and has been used in stroke rehabilitation. This study aimed to investigate the efficacy and safety of BCI in rehabilitation training on upper limb motor function among patients with ischemic stroke. METHODS This was an investigator-initiated, multicenter, randomized, open-label, blank-controlled clinical trial with blinded outcome assessment conducted at 17 centers in China. Patients were assigned in a 1:1 ratio to the BCI group or the control group based on traditional rehabilitation training. The primary efficacy outcome is the difference in improvement of the Fugl-Meyer Assessment upper extremity (FMA-UE) score between two groups at month 1 after randomization. The safety outcomes were any adverse events within 3 months. FINDINGS A total of 296 patients with ischemic stroke were enrolled and randomly allocated to the BCI group (n = 150) and the control group (n = 146). The primary efficacy outcomes of FMA-UE score change from baseline to 1 month were 13.17 (95% confidence interval [CI], 11.56-14.79) in the BCI group and 9.83 (95% CI, 8.19-11.47) in the control group (mean difference between groups was 3.35; 95% CI, 1.05-5.65; p = 0.0045). Adverse events occurred in 33 patients (22.00%) in the BCI group and in 31 patients (21.23%) in the control group. CONCLUSIONS BCI rehabilitation training can further improve upper limb motor function based on traditional rehabilitation training in patients with ischemic stroke. This study was registered at ClinicalTrials.gov: NCT04387474. FUNDING This work was supported by the National Key R&D Program of China (2018YFC1312903), the National Key Research and Development Program of China (2022YFC3600600), the Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University (CCMU2022ZKYXZ009), the Beijing Natural Science Foundation Haidian original innovation joint fund (L222123), the Fund for Young Talents of Beijing Medical Management Center (QML20230505), and the high-level public health talents (xuekegugan-02-47).
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Affiliation(s)
- Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xue Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China; Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China
| | - Di Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chengyuan Yang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qin Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yifei Zhang
- Research and Development Center, Shandong Haitian Intelligent Engineering Co., Ltd., Shandong, China
| | - Shaoqing Zhao
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Xiaoli Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Jing
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ning Wei
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuqian Wu
- Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Lv
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Banghua Yang
- School of Mechatronic Engineering and Automation, Research Center of Brain Computer Engineering, Shanghai University, Shanghai, China
| | - Dawei Zang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yilong Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yumei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yongjun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xia Meng
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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Lima JPS, Silva LA, Delisle-Rodriguez D, Cardoso VF, Nakamura-Palacios EM, Bastos-Filho TF. Unraveling Transformative Effects after tDCS and BCI Intervention in Chronic Post-Stroke Patient Rehabilitation-An Alternative Treatment Design Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:9302. [PMID: 38067674 PMCID: PMC10708803 DOI: 10.3390/s23239302] [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: 10/11/2023] [Revised: 11/10/2023] [Accepted: 11/16/2023] [Indexed: 12/18/2023]
Abstract
Stroke is a debilitating clinical condition resulting from a brain infarction or hemorrhage that poses significant challenges for motor function restoration. Previous studies have shown the potential of applying transcranial direct current stimulation (tDCS) to improve neuroplasticity in patients with neurological diseases or disorders. By modulating the cortical excitability, tDCS can enhance the effects of conventional therapies. While upper-limb recovery has been extensively studied, research on lower limbs is still limited, despite their important role in locomotion, independence, and good quality of life. As the life and social costs due to neuromuscular disability are significant, the relatively low cost, safety, and portability of tDCS devices, combined with low-cost robotic systems, can optimize therapy and reduce rehabilitation costs, increasing access to cutting-edge technologies for neuromuscular rehabilitation. This study explores a novel approach by utilizing the following processes in sequence: tDCS, a motor imagery (MI)-based brain-computer interface (BCI) with virtual reality (VR), and a motorized pedal end-effector. These are applied to enhance the brain plasticity and accelerate the motor recovery of post-stroke patients. The results are particularly relevant for post-stroke patients with severe lower-limb impairments, as the system proposed here provides motor training in a real-time closed-loop design, promoting cortical excitability around the foot area (Cz) while the patient directly commands with his/her brain signals the motorized pedal. This strategy has the potential to significantly improve rehabilitation outcomes. The study design follows an alternating treatment design (ATD), which involves a double-blind approach to measure improvements in both physical function and brain activity in post-stroke patients. The results indicate positive trends in the motor function, coordination, and speed of the affected limb, as well as sensory improvements. The analysis of event-related desynchronization (ERD) from EEG signals reveals significant modulations in Mu, low beta, and high beta rhythms. Although this study does not provide conclusive evidence for the superiority of adjuvant mental practice training over conventional therapy alone, it highlights the need for larger-scale investigations.
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Affiliation(s)
- Jéssica P. S. Lima
- Postgraduate Program in Biotechnology, Federal University of Espirito Santo (UFES), Vitoria 29047-105, Brazil; (J.P.S.L.); (V.F.C.); (T.F.B.-F.)
| | - Leticia A. Silva
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria 29075-910, Brazil;
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Neuroengineering, Edmond and Lily Safra International Institute of Neurosciences, Macaiba 59288-899, Brazil
| | - Vivianne F. Cardoso
- Postgraduate Program in Biotechnology, Federal University of Espirito Santo (UFES), Vitoria 29047-105, Brazil; (J.P.S.L.); (V.F.C.); (T.F.B.-F.)
| | - Ester M. Nakamura-Palacios
- Laboratory of Cognitive Sciences and Neuropsychopharmacology, Federal University of Espírito Santo, Vitoria 29040-090, Brazil;
| | - Teodiano F. Bastos-Filho
- Postgraduate Program in Biotechnology, Federal University of Espirito Santo (UFES), Vitoria 29047-105, Brazil; (J.P.S.L.); (V.F.C.); (T.F.B.-F.)
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), Vitoria 29075-910, Brazil;
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Zapała D, Augustynowicz P, Tokovarov M, Iwanowicz P, Droździel P. Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery. Neuroscience 2023; 532:37-49. [PMID: 37625688 DOI: 10.1016/j.neuroscience.2023.08.022] [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: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.
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Affiliation(s)
- Dariusz Zapała
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paweł Augustynowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | | | - Paulina Iwanowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paulina Droździel
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
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Gu B, Wang K, Chen L, He J, Zhang D, Xu M, Wang Z, Ming D. Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities. Neuroscience 2023; 530:56-65. [PMID: 37652289 DOI: 10.1016/j.neuroscience.2023.08.032] [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: 02/01/2023] [Revised: 07/13/2023] [Accepted: 08/25/2023] [Indexed: 09/02/2023]
Abstract
Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.
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Affiliation(s)
- Bin Gu
- SUISHI (Tianjin) Intelligence Ltd, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China
| | - Kun Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China.
| | - Long Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China.
| | - Jiatong He
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Dingze Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China
| | - Zhongpeng Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, China
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5
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Saha S, Baumert M, McEwan A. Can Inter-Subject Associativity Predict Data-Driven BCI Performance? ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082677 DOI: 10.1109/embc40787.2023.10340490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Intra- and inter-subject variability causes covariate shifts in training and testing feature spaces, resulting in low sensorimotor (SMR) brain-computer interface (BCI) performance for practical implementation. Studies involving data-driven transfer learning strategies demonstrated improving BCI performance by covariate shift adaptation. In this study, we aim to illustrate if inter-subject associativity (e.g., subjects having similar SMR brain dynamics) can predict data-driven inter-subject BCI performance. We implemented a BCI classification pipeline with a common spatial pattern, principal component analysis and linear discriminant analysis for performance evaluation. Both intra- and inter-subject BCI were evaluated in 5-Fold Validation settings. We further proposed a Bhattacharyya distance-based covariate shift score (CSS) for assessing the difference between training and testing feature domains. We performed Pearson correlation analysis to draw the relation-ship between BCI performance and CSS. Intra-subject BCI performances were significantly and negatively correlated with CSS (r = -0.94, p < 0.05). For the inter-subject experiment, BCI performances were also highly and negatively associated with CSS (r = -0.61, p < 0.05). However, this data-driven BCI evaluation framework does not necessarily manifest inter-subject associativity in BCI performance, requiring further investigations for a conclusion.Clinical relevance- If it predicts BCI performance successfully, inter-subject associativity could reduce time-consuming and annoying subject-specific calibration for the users.
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Guerrero-Mendez CD, Blanco-Diaz CF, Ruiz-Olaya AF, López-Delis A, Jaramillo-Isaza S, Milanezi Andrade R, Ferreira De Souza A, Delisle-Rodriguez D, Frizera-Neto A, Bastos-Filho TF. EEG motor imagery classification using deep learning approaches in naïve BCI users. Biomed Phys Eng Express 2023; 9:045029. [PMID: 37321179 DOI: 10.1088/2057-1976/acde82] [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: 02/21/2023] [Accepted: 06/15/2023] [Indexed: 06/17/2023]
Abstract
Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the use. To reduce the effects of lack of experience in the use of BCI systems (naïve users), this paper presents the implementation of three Deep Learning (DL) methods with the hypothesis that the performance of BCI systems could be improved compared with baseline methods in the evaluation of naïve BCI users. The methods proposed here are based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM)/Bidirectional Long Short-Term Memory (BiLSTM), and a combination of CNN and LSTM used for upper limb MI signal discrimination on a dataset of 25 naïve BCI users. The results were compared with three widely used baseline methods based on the Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP), and Filter Bank Common Spatial-Spectral Pattern (FBCSSP), in different temporal window configurations. As results, the LSTM-BiLSTM-based approach presented the best performance, according to the evaluation metrics of Accuracy, F-score, Recall, Specificity, Precision, and ITR, with a mean performance of 80% (maximum 95%) and ITR of 10 bits/min using a temporal window of 1.5 s. The DL Methods represent a significant increase of 32% compared with the baseline methods (p< 0.05). Thus, with the outcomes of this study, it is expected to increase the controllability, usability, and reliability of the use of robotic devices in naïve BCI users.
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Affiliation(s)
- Cristian D Guerrero-Mendez
- Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Cristian F Blanco-Diaz
- Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Andres F Ruiz-Olaya
- Faculty of Mechanical, Electronic and Biomedical Engineering, Antonio Nariño University (UAN), Bogotá, Colombia
| | - Alberto López-Delis
- Center of Medical Biophysics, Universidad de Oriente, Santiado de Cuba, Cuba
| | - Sebastian Jaramillo-Isaza
- Faculty of Mechanical, Electronic and Biomedical Engineering, Antonio Nariño University (UAN), Bogotá, Colombia
| | - Rafhael Milanezi Andrade
- Graduate Program in Mechanical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | | | - Denis Delisle-Rodriguez
- Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, Macaiba-RN, Brazil
| | - Anselmo Frizera-Neto
- Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Teodiano F Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espírito Santo (UFES), Vitória, Brazil
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Wang W, Shi B, Wang D, Wang J, Liu G. Enhanced lower-limb motor imagery by kinesthetic illusion. Front Neurosci 2023; 17:1077479. [PMID: 37409102 PMCID: PMC10319417 DOI: 10.3389/fnins.2023.1077479] [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: 10/23/2022] [Accepted: 05/30/2023] [Indexed: 07/07/2023] Open
Abstract
Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.
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Affiliation(s)
- Weizhen Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Bin Shi
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Dong Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Jing Wang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
| | - Gang Liu
- Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
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Delisle-Rodriguez D, Silva L, Bastos-Filho T. EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration. J Neural Eng 2023; 20. [PMID: 36716494 DOI: 10.1088/1741-2552/acb73b] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 01/30/2023] [Indexed: 01/31/2023]
Abstract
Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.
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Affiliation(s)
- Denis Delisle-Rodriguez
- Edmond and Lily Safra International Institute of Neurosciences, Santos Dumont Institute, 59288-899 Macaiba, Brazil
| | - Leticia Silva
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, 29075-910 Vitoria, Brazil
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Li H, Liu M, Yu X, Zhu J, Wang C, Chen X, Feng C, Leng J, Zhang Y, Xu F. Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury. Front Neurosci 2023; 16:1097660. [PMID: 36711141 PMCID: PMC9880407 DOI: 10.3389/fnins.2022.1097660] [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: 11/14/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Background Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients. Methods According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group. Results The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%. Conclusion The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.
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Affiliation(s)
- Han Li
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - JianQun Zhu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Xinyi Chen
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Chao Feng
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,*Correspondence: Chao Feng,
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,Jiancai Leng,
| | - Yang Zhang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China,Yang Zhang,
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,Fangzhou Xu,
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Wang K, Tian F, Xu M, Zhang S, Xu L, Ming D. Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1556. [PMID: 36359646 PMCID: PMC9689965 DOI: 10.3390/e24111556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/23/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.
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Affiliation(s)
- Kun Wang
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Feifan Tian
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Minpeng Xu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
| | - Shanshan Zhang
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Lichao Xu
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China
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11
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Nakano H, Tachibana M, Fujita N, Sawai S, Fujikawa S, Yamamoto R, Murata S. Reliability and validity of the Japanese movement imagery questionnaire-revised second version. BMC Res Notes 2022; 15:334. [PMID: 36284354 PMCID: PMC9594881 DOI: 10.1186/s13104-022-06220-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 09/14/2022] [Accepted: 09/21/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Developing a Japanese version of the Movement Imagery Questionnaire-Revised Second Version (MIQ-RS) is essential for widespread evaluation and treatment based on motor imagery in physically disabled persons and patients in rehabilitation. This study aimed to investigate the reliability and validity of the Movement Imagery Questionnaire-Revised Second Version (MIQ-RS), which assesses motor imagery ability, by translating it into Japanese. Results This study enrolled twenty healthy participants (10 men and 10 women, mean age 21.17 ± 1.10 years). Reliability was examined for internal consistency using Cronbach’s alpha coefficient. Spearman’s rank correlation coefficient was used to examine the criterion-related validity of the MIQ-RS and the Kinesthetic and Visual Imagery Questionnaire (KVIQ-20). Results showed that Cronbach’s alpha coefficients for the MIQ-RS were 0.81 and 0.82 for visual and kinesthetic imagery, respectively. Significant positive correlations were found between each visual and kinesthetic imagery score, and each total on the MIQ-RS and KVIQ-20 scores (r = 0.73, p < 0.01; r = 0.84, p < 0.01; r = 0.80, p < 0.01, respectively). This study suggests that the Japanese version of the MIQ-RS is a reliable and valid method of assessing motor imagery ability. Supplementary Information The online version contains supplementary material available at 10.1186/s13104-022-06220-y.
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Affiliation(s)
- Hideki Nakano
- Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan. .,Department of Physical Therapy, Faculty of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan.
| | - Mizuki Tachibana
- Department of Physical Therapy, Faculty of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan
| | - Nao Fujita
- Department of Physical Therapy, Faculty of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan
| | - Shun Sawai
- Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan.,Department of Rehabilitation, Kyoto Kuno Hospital, 22-500 Honmachi, Higashiyama-ku, 605-0981, Kyoto-city, Kyoto, Japan
| | - Shoya Fujikawa
- Department of Physical Therapy, Faculty of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan
| | - Ryosuke Yamamoto
- Department of Rehabilitation, Tesseikai Neurosurgical Hospital, 28-1 Nakanohonmachi, 575-8511, Shijonawate-city, Osaka, Japan
| | - Shin Murata
- Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan.,Department of Physical Therapy, Faculty of Health Sciences, Kyoto Tachibana University, 34 Yamada-cho, Oyake, Yamashina-ku, 607-8175, Kyoto-city, Kyoto, Japan
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12
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Le Franc S, Herrera Altamira G, Guillen M, Butet S, Fleck S, Lécuyer A, Bougrain L, Bonan I. Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives. Front Hum Neurosci 2022; 16:917909. [PMID: 35911589 PMCID: PMC9332194 DOI: 10.3389/fnhum.2022.917909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.
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Affiliation(s)
- Salomé Le Franc
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- *Correspondence: Salomé Le Franc,
| | | | - Maud Guillen
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
- Neurology Unit, University Hospital of Rennes, Rennes, France
| | - Simon Butet
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | - Stéphanie Fleck
- Université de Lorraine, CNRS, LORIA, Nancy, France
- EA7312 Laboratoire de Psychologie Ergonomique et Sociale pour l’Expérience Utilisateurs (PERSEUS), Metz, France
| | - Anatole Lécuyer
- Hybrid Team, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
| | | | - Isabelle Bonan
- Rehabilitation Medicine Unit, University Hospital of Rennes, Rennes, France
- Empenn Unit U1228, Inserm, Inria, University of Rennes, Irisa, UMR CNRS 6074, Rennes, France
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13
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Rimbert S, Lotte F. ERD modulations during motor imageries relate to users' traits and BCI performances. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:203-207. [PMID: 36086209 DOI: 10.1109/embc48229.2022.9871411] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Improving user performances is one of the major issues for Motor Imagery (MI) - based BCI control. MI-BCIs exploit the modulation of sensorimotor rhythms (SMR) over the motor and sensorimotor cortices to discriminate several mental states and enable user interaction. Such modulations are known as Event-Related Desynchronization (ERD) and Synchronization (ERS), coming from the mu (7-13 Hz) and beta (15-30 Hz) frequency bands. This kind of BCI opens up promising fields, particularly to control assistive technologies, for sport training or even for post-stroke motor rehabilitation. However, MI - BCIs remain barely used outside laboratories, notably due to their lack of robustness and usability (15 to 30% of users seem unable to gain control of an MI-BCI). One way to increase user performance would be to better understand the relationships between user traits and ERD/ERS modulations underlying BCI performance. Therefore, in this article we analyzed how cerebral motor patterns underlying MI tasks (i.e., ERDs and ERSs) are modulated depending (i) on nature of the task (i.e., right-hand MI and left-hand MI), (ii) the session during which the task was performed (i.e., calibration or user training) and (iii) on the characteristics of the user (e.g., age, gender, manual activity, personality traits) on a large MI-BCI data base of N=75 participants. One of the originality of this study is to combine the investigation of human factors related to the user's traits and the neurophysiological ERD modulations during the MI task. Our study revealed for the first time an association between ERD and self-control from the 16PF5 questionnaire.
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14
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Xu F, Wang Y, Li H, Yu X, Wang C, Liu M, Jiang L, Feng C, Li J, Wang D, Yan Z, Zhang Y, Leng J. Time-Varying Effective Connectivity for Describing the Dynamic Brain Networks of Post-stroke Rehabilitation. Front Aging Neurosci 2022; 14:911513. [PMID: 35686023 PMCID: PMC9171495 DOI: 10.3389/fnagi.2022.911513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.
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Affiliation(s)
- Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- *Correspondence: Fangzhou Xu
| | - Yuandong Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Han Li
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, Ministry of Education Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chao Feng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Jianfei Li
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
| | - Dezheng Wang
- The Department of Physical Medicine and Rehabilitation, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Zhiguo Yan
- School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Zhiguo Yan
| | - Yang Zhang
- The Department of Physical Medicine and Rehabilitation, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
- Yang Zhang
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Jiancai Leng
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15
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Sugino H, Ushiyama J. Gymnasts' Ability to Modulate Sensorimotor Rhythms During Kinesthetic Motor Imagery of Sports Non-specific Movements Superior to Non-gymnasts. Front Sports Act Living 2021; 3:757308. [PMID: 34805979 PMCID: PMC8600039 DOI: 10.3389/fspor.2021.757308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 09/30/2021] [Indexed: 11/30/2022] Open
Abstract
Previous psychological studies using questionnaires have consistently reported that athletes have superior motor imagery ability, both for sports-specific and for sports-non-specific movements. However, regarding motor imagery of sports-non-specific movements, no physiological studies have demonstrated differences in neural activity between athletes and non-athletes. The purpose of this study was to examine the differences in sensorimotor rhythms during kinesthetic motor imagery (KMI) of sports-non-specific movements between gymnasts and non-gymnasts. We selected gymnasts as an example population because they are likely to have particularly superior motor imagery ability due to frequent usage of motor imagery, including KMI as part of daily practice. Healthy young participants (16 gymnasts and 16 non-gymnasts) performed repeated motor execution and KMI of sports-non-specific movements (wrist dorsiflexion and shoulder abduction of the dominant hand). Scalp electroencephalogram (EEG) was recorded over the contralateral sensorimotor cortex. During motor execution and KMI, sensorimotor EEG power is known to decrease in the α- (8–15 Hz) and β-bands (16–35 Hz), referred to as event-related desynchronization (ERD). We calculated the maximal peak of ERD both in the α- (αERDmax) and β-bands (βERDmax) as a measure of changes in corticospinal excitability. αERDmax was significantly greater in gymnasts, who subjectively evaluated their KMI as being more vivid in the psychological questionnaire. On the other hand, βERDmax was greater in gymnasts only for shoulder abduction KMI. These findings suggest gymnasts' signature of flexibly modulating sensorimotor rhythms with no movements, which may be the basis of their superior ability of KMI for sports-non-specific movements.
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Affiliation(s)
- Hirotaka Sugino
- Graduate School of Media and Governance, Keio University, Fujisawa, Japan
| | - Junichi Ushiyama
- Faculty of Environment and Information Studies, Keio University, Fujisawa, Japan.,Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan
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16
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Vasilyev AN, Nuzhdin YO, Kaplan AY. Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice? Brain Sci 2021; 11:brainsci11091234. [PMID: 34573253 PMCID: PMC8469546 DOI: 10.3390/brainsci11091234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/16/2021] [Accepted: 09/16/2021] [Indexed: 11/23/2022] Open
Abstract
Background. Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain’s involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or—even better—to turn them into actions through a technology called the brain–computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. Methods. Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. Results. The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson’s ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. Conclusion. The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.
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Affiliation(s)
- Anatoly N. Vasilyev
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
- MEG Center, Moscow State University of Psychology and Education, 123290 Moscow, Russia
- Correspondence:
| | - Yury O. Nuzhdin
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
| | - Alexander Y. Kaplan
- Department of Human and Animal Physiology, Faculty of Biology, M.V. Lomonosov Moscow State University, 119234 Moscow, Russia; (Y.O.N.); (A.Y.K.)
- Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
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17
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Deep and Wide Transfer Learning with Kernel Matching for Pooling Data from Electroencephalography and Psychological Questionnaires. SENSORS 2021; 21:s21155105. [PMID: 34372338 PMCID: PMC8347227 DOI: 10.3390/s21155105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/03/2021] [Accepted: 07/09/2021] [Indexed: 12/22/2022]
Abstract
Motor imagery (MI) promotes motor learning and encourages brain–computer interface systems that entail electroencephalogram (EEG) decoding. However, a long period of training is required to master brain rhythms’ self-regulation, resulting in users with MI inefficiency. We introduce a parameter-based approach of cross-subject transfer-learning to improve the performances of poor-performing individuals in MI-based BCI systems, pooling data from labeled EEG measurements and psychological questionnaires via kernel-embedding. To this end, a Deep and Wide neural network for MI classification is implemented to pre-train the network from the source domain. Then, the parameter layers are transferred to initialize the target network within a fine-tuning procedure to recompute the Multilayer Perceptron-based accuracy. To perform data-fusion combining categorical features with the real-valued features, we implement stepwise kernel-matching via Gaussian-embedding. Finally, the paired source–target sets are selected for evaluation purposes according to the inefficiency-based clustering by subjects to consider their influence on BCI motor skills, exploring two choosing strategies of the best-performing subjects (source space): single-subject and multiple-subjects. Validation results achieved for discriminant MI tasks demonstrate that the introduced Deep and Wide neural network presents competitive performance of accuracy even after the inclusion of questionnaire data.
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18
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Zapała D, Iwanowicz P, Francuz P, Augustynowicz P. Handedness effects on motor imagery during kinesthetic and visual-motor conditions. Sci Rep 2021; 11:13112. [PMID: 34162936 PMCID: PMC8222290 DOI: 10.1038/s41598-021-92467-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 06/09/2021] [Indexed: 11/17/2022] Open
Abstract
Recent studies show that during a simple movement imagery task, the power of sensorimotor rhythms differs according to handedness. However, the effects of motor imagery perspectives on these differences have not been investigated yet. Our study aimed to check how handedness impacts the activity of alpha (8-13 Hz) and beta (15-30 Hz) oscillations during creating a kinesthetic (KMI) or visual-motor (VMI) representation of movement. Forty subjects (20 right-handed and 20 left-handed) who participated in the experiment were tasked with imagining sequential finger movement from a visual or kinesthetic perspective. Both the electroencephalographic (EEG) activity and behavioral correctness of the imagery task performance were measured. After the registration, we used independent component analysis (ICA) on EEG data to localize visual- and motor-related EEG sources of activity shared by both motor imagery conditions. Significant differences were obtained in the visual cortex (the occipital ICs cluster) and the right motor-related area (right parietal ICs cluster). In comparison to right-handers who, regardless of the task, demonstrated the same pattern in the visual area, left-handers obtained higher power in the alpha waves in the VMI task and better performance in this condition. On the other hand, only the right-handed showed different patterns in the alpha waves in the right motor cortex during the KMI condition. The results indicate that left-handers imagine movement differently than right-handers, focusing on visual experience. This provides new empirical evidence on the influence of movement preferences on imagery processes and has possible future implications for research in the area of neurorehabilitation and motor imagery-based brain-computer interfaces (MI-BCIs).
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Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950, Lublin, Poland.
| | - Paulina Iwanowicz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950, Lublin, Poland
| | - Piotr Francuz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950, Lublin, Poland
| | - Paweł Augustynowicz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950, Lublin, Poland
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19
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Zapała D, Hossaini A, Kianpour M, Sahonero-Alvarez G, Ayesh A. A functional BCI model by the P2731 working group: psychology. BRAIN-COMPUTER INTERFACES 2021. [DOI: 10.1080/2326263x.2021.1935124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Lublin, Poland
| | - Ali Hossaini
- Department of Engineering, King’s College London, London, UK
| | - Mazaher Kianpour
- Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway
| | - Guillermo Sahonero-Alvarez
- Center for Research, Development and Innovation in Mechatronics Engineering,Department of Mechatronics Engineering, Universidad Catolica Boliviana San Pablo, La Paz, Bolivia
| | - Aladdin Ayesh
- Faculty of Computing,Engineering and Media,De Montfort University, Leicester, UK
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20
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Leeuwis N, Paas A, Alimardani M. Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces. Front Hum Neurosci 2021; 15:634748. [PMID: 33889080 PMCID: PMC8055841 DOI: 10.3389/fnhum.2021.634748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Brain-computer interfaces (BCIs) are communication bridges between a human brain and external world, enabling humans to interact with their environment without muscle intervention. Their functionality, therefore, depends on both the BCI system and the cognitive capacities of the user. Motor-imagery BCIs (MI-BCI) rely on the users' mental imagination of body movements. However, not all users have the ability to sufficiently modulate their brain activity for control of a MI-BCI; a problem known as BCI illiteracy or inefficiency. The underlying mechanism of this phenomenon and the cause of such difference among users is yet not fully understood. In this study, we investigated the impact of several cognitive and psychological measures on MI-BCI performance. Fifty-five novice BCI-users participated in a left- versus right-hand motor imagery task. In addition to their BCI classification error rate and demographics, psychological measures including personality factors, affinity for technology, and motivation during the experiment, as well as cognitive measures including visuospatial memory and spatial ability and Vividness of Visual Imagery were collected. Factors that were found to have a significant impact on MI-BCI performance were Vividness of Visual Imagery, and the personality factors of orderliness and autonomy. These findings shed light on individual traits that lead to difficulty in BCI operation and hence can help with early prediction of inefficiency among users to optimize training for them.
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Affiliation(s)
- Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, Netherlands
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21
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Effect of a Brain-Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity. SENSORS 2021; 21:s21062020. [PMID: 33809317 PMCID: PMC8000427 DOI: 10.3390/s21062020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/05/2021] [Accepted: 03/07/2021] [Indexed: 12/21/2022]
Abstract
Recently, studies on cycling-based brain–computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.
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22
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Pitt KM, Brumberg JS. Evaluating person-centered factors associated with brain-computer interface access to a commercial augmentative and alternative communication paradigm. Assist Technol 2021; 34:468-477. [PMID: 33667154 DOI: 10.1080/10400435.2021.1872737] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022] Open
Abstract
Current BCI-AAC systems largely utilize custom-made software and displays that may be unfamiliar to AAC stakeholders. Further, there is limited information available exploring the heterogenous profiles of individuals who may use BCI-AAC. Therefore, in this study, we aimed to evaluate how individuals with amyotrophic lateral sclerosis (ALS) learned to control a motor-based BCI switch in a row-column AAC scanning pattern, and person-centered factors associated with BCI-AAC performance. Four individuals with ALS completed 12 BCI-AAC training sessions, and three individuals without neurological impairment completed 3 BCI-AAC training sessions. To assess person-centered factors associated with BCI-AAC performance, participants completed both initial and recurring assessment measures including levels of cognition, motor ability, fatigue, and motivation. Three of four participants demonstrated either BCI-AAC performance in the range of neurotypical peers, or an improving BCI-AAC learning trajectory. However, BCI-AAC learning trajectories were variable. Assessment measures revealed that two participants presented with a suspicion for cognitive impairment yet achieved the highest levels of BCI-AAC accuracy with their increased levels of performance being possibly supported by largely unimpaired motor skills. Motor-based BCI switch access to a commercial AAC row-column scanning may be feasible for individuals with ALS and possibly supported by timely intervention.
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Affiliation(s)
- Kevin M Pitt
- Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jonathan S Brumberg
- Department of Speech-Language-Hearing: Sciences & Disorders, University of Kansas, Lawrence, Kansas, USA
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23
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Zhang R, Li F, Zhang T, Yao D, Xu P. Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions. BRAIN SCIENCE ADVANCES 2021. [DOI: 10.26599/bsa.2020.9050021] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Motor imagery brain–computer interfaces (MI‐BCIs) have great potential value in prosthetics control, neurorehabilitation, and gaming; however, currently, most such systems only operate in controlled laboratory environments. One of the most important obstacles is the MI‐BCI inefficiency phenomenon. The accuracy of MI‐BCI control varies significantly (from chance level to 100% accuracy) across subjects due to the not easily induced and unstable MI‐related EEG features. An MI‐BCI inefficient subject is defined as a subject who cannot achieve greater than 70% accuracy after sufficient training time, and multiple survey results indicate that inefficient subjects account for 10%–50% of the experimental population. The widespread use of MI‐BCI has been seriously limited due to these large percentages of inefficient subjects. In this review, we summarize recent findings of the cause of MI‐BCI inefficiency from resting‐state brain function, task‐related brain activity, brain structure, and psychological perspectives. These factors help understand the reasons for inter‐subject MI‐BCI control performance variability, and it can be concluded that the lower resting‐state sensorimotor rhythm (SMR) is the key factor in MI‐BCI inefficiency, which has been confirmed by multiple independent laboratories. We then propose to divide MI‐BCI inefficient subjects into three categories according to the resting‐state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem. The potential solutions include developing transfer learning algorithms, new experimental paradigms, mindfulness meditation practice, novel training strategies, and identifying new motor imagery‐related EEG features. To date, few studies have focused on improving the control accuracy of MI‐BCI inefficient subjects; thus, we appeal to the BCI community to focus more on this research area. Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI‐BCI.
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Affiliation(s)
- Rui Zhang
- Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
| | - Fali Li
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
| | - Tao Zhang
- Science of School, Xihua University, Chengdu 610039, Sichuan, China
| | - Dezhong Yao
- Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, Henan, China
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
| | - Peng Xu
- MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China
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24
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Roc A, Pillette L, Mladenovic J, Benaroch C, N'Kaoua B, Jeunet C, Lotte F. A review of user training methods in brain computer interfaces based on mental tasks. J Neural Eng 2020; 18. [PMID: 33181488 DOI: 10.1088/1741-2552/abca17] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 11/12/2020] [Indexed: 12/12/2022]
Abstract
Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training - notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.
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Affiliation(s)
| | | | | | - Camille Benaroch
- Inria Centre de recherche Bordeaux Sud-Ouest, Talence, 33405, FRANCE
| | - Bernard N'Kaoua
- Handicap, Activity, Cognition, Health, Inserm / University of Bordeaux, Talence, FRANCE
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25
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Zapparoli L, Seghezzi S, Sacheli LM, Verga C, Banfi G, Paulesu E. Eyes wide shut: How visual cues affect brain patterns of simulated gait. Hum Brain Mapp 2020; 41:4248-4263. [PMID: 32639101 PMCID: PMC7502842 DOI: 10.1002/hbm.25123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/11/2020] [Accepted: 06/22/2020] [Indexed: 11/09/2022] Open
Abstract
In the last 20 years, motor imagery (MI) has been extensively used to train motor abilities in sport and in rehabilitation. However, MI procedures are not all alike as much as their potential beneficiaries. Here we assessed whether the addition of visual cues could make MI performance more comparable with explicit motor performance in gait tasks. With fMRI we also explored the neural correlates of these experimental manipulations. We did this in elderly subjects who are known to rely less on kinesthetic information while favoring visual strategies during motor performance. Contrary to expectations, we found that the temporal coupling between execution and imagery times, an index of the quality of MI, was less precise when participants were allowed to visually explore the environment. While the brain activation patterns of the gait motor circuits were very similar in both an open‐eyed and eye‐shut virtual walking MI task, these differed for a vast temporo‐occipito‐parietal additional activation for open‐eyed MI. Crucially, the higher was the activity in this posterior network, the less accurate was the MI performance with eyes open at a clinical test of gait. We conclude that both visually‐cued and internally‐cued MI are associated with the neurofunctional activation of a gait specific motor system. The less precise behavioral coupling between imagined and executed gait while keeping eyes open may be attributed to the processing load implied in visual monitoring and scanning of the environment. The implications of these observations for rehabilitation of gait with MI are discussed.
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Affiliation(s)
- Laura Zapparoli
- Psychology Department & Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy.,fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Silvia Seghezzi
- Psychology Department & Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy.,PhD Program in Neuroscience of School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Lucia Maria Sacheli
- Psychology Department & Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy.,fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Chiara Verga
- Psychology Department & Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Giuseppe Banfi
- fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.,School of Medicine, San Raffaele Vita e Salute University, Milan, Italy
| | - Eraldo Paulesu
- Psychology Department & Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy.,fMRI Unit, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
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26
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Zapała D, Małkiewicz M, Francuz P, Kołodziej M, Majkowski A. Temperament Predictors of Motor Imagery Control in BCI. J PSYCHOPHYSIOL 2020. [DOI: 10.1027/0269-8803/a000252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract. The aim of this study was to verify if selected temperament traits may be useful as predictors of motor imagery brain-computer interface (BCI) performance. In our experiment, 40 BCI-naive subjects were instructed to imagine the movement of clenching his/her right or left hand, in accordance with the visual cue. The activity of sensorimotor rhythms (SMR) (8–30 Hz) was measured by electroencephalography (EEG) and transformed into the information transfer rate (ITR) after feature selection and classification. All subjects also completed a self-assessment questionnaire for the determination of their temperament profile, comprising the following traits: Briskness, Perseveration, Sensory Sensitivity, Emotional Reactivity, Endurance, and Activity. We found significant correlations between ITR performance and Endurance (EN) and Perseveration (PE) scores. This effect was also visible in a topography of SMR desynchronization patterns, in groups with different results in EN and PE scales. Finally, a predictive model of motor imagery BCI control based on temperament traits was proposed. We interpret this finding as empirical support for an influence of basic, relatively stable personality traits on BCI control via the performance of the motor imagery task. Moreover, the implication of these results on the design of future brain-computer interfaces was discussed.
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Affiliation(s)
- Dariusz Zapała
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Poland
| | - Monika Małkiewicz
- Institute of Psychology, Cardinal Stefan Wyszynski University, Warsaw, Poland
| | - Piotr Francuz
- Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Poland
| | - Marcin Kołodziej
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
| | - Andrzej Majkowski
- Institute of Theory of Electrical Engineering, Measurement and Information Systems, Warsaw University of Technology, Warsaw, Poland
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27
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The effects of handedness on sensorimotor rhythm desynchronization and motor-imagery BCI control. Sci Rep 2020; 10:2087. [PMID: 32034277 PMCID: PMC7005877 DOI: 10.1038/s41598-020-59222-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/27/2020] [Indexed: 11/17/2022] Open
Abstract
Brain–computer interfaces (BCIs) allow control of various applications or external devices solely by brain activity, e.g., measured by electroencephalography during motor imagery. Many users are unable to modulate their brain activity sufficiently in order to control a BCI. Most of the studies have been focusing on improving the accuracy of BCI control through advances in signal processing and BCI protocol modification. However, some research suggests that motor skills and physiological factors may affect BCI performance as well. Previous studies have indicated that there is differential lateralization of hand movements’ neural representation in right- and left-handed individuals. However, the effects of handedness on sensorimotor rhythm (SMR) distribution and BCI control have not been investigated in detail yet. Our study aims to fill this gap, by comparing the SMR patterns during motor imagery and real-feedback BCI control in right- (N = 20) and left-handers (N = 20). The results of our study show that the lateralization of SMR during a motor imagery task differs according to handedness. Left-handers present lower accuracy during BCI performance (single session) and weaker SMR suppression in the alpha band (8–13 Hz) during mental simulation of left-hand movements. Consequently, to improve BCI control, the user’s training should take into account individual differences in hand dominance.
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28
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Spychala N, Debener S, Bongartz E, Müller HHO, Thorne JD, Philipsen A, Braun N. Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation. Front Hum Neurosci 2020; 13:461. [PMID: 32038198 PMCID: PMC6984194 DOI: 10.3389/fnhum.2019.00461] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 12/16/2019] [Indexed: 12/14/2022] Open
Abstract
Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.
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Affiliation(s)
- Nadine Spychala
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Edith Bongartz
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Helge H O Müller
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jeremy D Thorne
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Alexandra Philipsen
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Niclas Braun
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
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29
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Carino-Escobar RI, Galicia-Alvarado M, Marrufo OR, Carrillo-Mora P, Cantillo-Negrete J. Brain-computer interface performance analysis of monozygotic twins with discordant hand dominance: A case study. Laterality 2020; 25:513-536. [PMID: 31918621 DOI: 10.1080/1357650x.2019.1710525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Brain-computer interfaces (BCI) decode user's intentions to control external devices. However, performance variations across individuals have limited their use to laboratory environments. Handedness could contribute to these variations, especially when motor imagery (MI) tasks are used for BCI control. To further understand how handedness affects BCI control, performance differences between two monozygotic twins were analysed during offline movement and MI tasks, and while twins controlled a BCI using right-hand MI. Quantitative electroencephalography (qEEG), brain structures' volumes, and neuropsychological tests were assessed to evaluate physiological, anatomical and psychological relationships with BCI performance. Results showed that both twins had good motor imagery and attention abilities, similar volumes on most subcortical brain structures, more pronounced event-related desynchronization elicited by the twin performing non-dominant MI, and that this twin also obtained significant higher performances with the BCI. Linear regression analysis implied a strong association between twins' BCI performance, and more pronounced cortical activations in the contralateral hemisphere relative to hand MI. Therefore, it is possible that BCI performance was related with the ability of each twin to elicit cortical activations during hand MI, and less associated with subcortical brain structures' volumes and neuropsychological tests.
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Affiliation(s)
- Ruben I Carino-Escobar
- Division of Research in Medical Engineering, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Marlene Galicia-Alvarado
- Department of Electrodiagnostic, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Oscar R Marrufo
- Department of Neuroimage, National Institute of Neurology and Neurosurgery "Manuel Velasco Suárez", Mexico City, Mexico
| | - Paul Carrillo-Mora
- Division of Neuroscience, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
| | - Jessica Cantillo-Negrete
- Division of Research in Medical Engineering, National Institute of Rehabilitation "Luis Guillermo Ibarra Ibarra", Mexico City, Mexico
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30
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Lotte F, Jeunet C, Chavarriaga R, Bougrain L, Thompson DE, Scherer R, Mowla MR, Kübler A, Grosse-Wentrup M, Dijkstra K, Dayan N. Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research. BRAIN-COMPUTER INTERFACES 2020. [DOI: 10.1080/2326263x.2019.1697143] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Fabien Lotte
- Inria, LaBRI, CNRS/University of Bordeaux/Bordeaux INP, Bordeaux, France
| | - Camille Jeunet
- CLLE Lab, CNRS, University of Toulouse Jean Jaurès, Toulouse, France
| | - Ricardo Chavarriaga
- Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | | | - Dave E. Thompson
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Reinhold Scherer
- Brain-Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Md Rakibul Mowla
- Brain and Body Sensing Laboratory, Department of Electrical and Computer Engineering, Kansas State University, Manhattan, KS, USA
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Wurzburg, Germany
| | - Moritz Grosse-Wentrup
- Research Group Neuroinformatics, Faculty of Computer Science, University of Vienna, Vienna, Austria
| | - Karen Dijkstra
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Natalie Dayan
- Intelligent Systems Research Center, Ulster University, Londonderry, Northern Ireland
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