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Kilmarx J, Tashev I, Millan JDR, Sulzer J, Lewis-Peacock J. Evaluating the Feasibility of Visual Imagery for an EEG-Based Brain-Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2209-2219. [PMID: 38843055 PMCID: PMC11249027 DOI: 10.1109/tnsre.2024.3410870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Visual imagery, or the mental simulation of visual information from memory, could serve as an effective control paradigm for a brain-computer interface (BCI) due to its ability to directly convey the user's intention with many natural ways of envisioning an intended action. However, multiple initial investigations into using visual imagery as a BCI control strategies have been unable to fully evaluate the capabilities of true spontaneous visual mental imagery. One major limitation in these prior works is that the target image is typically displayed immediately preceding the imagery period. This paradigm does not capture spontaneous mental imagery as would be necessary in an actual BCI application but something more akin to short-term retention in visual working memory. Results from the present study show that short-term visual imagery following the presentation of a specific target image provides a stronger, more easily classifiable neural signature in EEG than spontaneous visual imagery from long-term memory following an auditory cue for the image. We also show that short-term visual imagery and visual perception share commonalities in the most predictive electrodes and spectral features. However, visual imagery received greater influence from frontal electrodes whereas perception was mostly confined to occipital electrodes. This suggests that visual perception is primarily driven by sensory information whereas visual imagery has greater contributions from areas associated with memory and attention. This work provides the first direct comparison of short-term and long-term visual imagery tasks and provides greater insight into the feasibility of using visual imagery as a BCI control strategy.
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Pereira DJ, Pereira J, Sayal A, Morais S, Macedo A, Direito B, Castelo-Branco M. Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks. Netw Neurosci 2024; 8:81-95. [PMID: 38562293 PMCID: PMC10861170 DOI: 10.1162/netn_a_00338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 09/10/2023] [Indexed: 04/04/2024] Open
Abstract
Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.
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Affiliation(s)
- Daniela Jardim Pereira
- Neurorradiology Functional Area, Imaging Department, Coimbra Hospital and University Center, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Pereira
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Alexandre Sayal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Siemens Healthineers Portugal, Lisboa, Portugal
| | - Sofia Morais
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Psychiatry Department, Coimbra Hospital and University Center, Coimbra, Portugal
| | - António Macedo
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Psychiatry Department, Coimbra Hospital and University Center, Coimbra, Portugal
| | - Bruno Direito
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
- Instituto do Ambiente, Tecnologia e Vida (IATV), Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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Jorajuria T, Nikulin VV, Kapralov N, Gomez M, Vidaurre C. MEAN SP: How Many Channels are Needed to Predict the Performance of a SMR-Based BCI? IEEE Trans Neural Syst Rehabil Eng 2023; 31:4931-4941. [PMID: 38051627 DOI: 10.1109/tnsre.2023.3339612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Predicting whether a particular individual would reach an adequate control of a Brain-Computer Interface (BCI) has many practical advantages. On the one hand, participants with low predicted performance could be trained with specifically designed sessions and avoid frustrating experiments; on the other hand, planning time and resources would be more efficient; and finally, the variables related to an accurate prediction could be manipulated to improve the prospective BCI performance. To this end, several predictors have been proposed in the literature, most of them based on the power estimation of EEG signals at the specific frequency bands. Many of these studies evaluate their predictors in relatively small datasets and/or using a relatively high number of channels. In this manuscript, we propose a novel predictor called [Formula: see text] to predict the performance of participants using BCIs that are based on the modulation of sensorimotor rhythms. This novel predictor has been positively evaluated using only 2, 3, 4 or 5 channels. [Formula: see text] has shown to perform as well as or better than other state-of-the-art predictors. The best sets of different number of channels are also provided, which have been tested in two different settings to prove their robustness. The proposed predictor has been successfully evaluated using two large-scale datasets containing 150 and 80 participants, respectively. We also discuss predictor thresholds for users to expect good performance in feedback experiments and show the advantages in comparison to a competing algorithm.
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Li M, Zuo H, Zhou H, Xu G, Qi E. A study of action difference on motor imagery based on delayed matching posture task. J Neural Eng 2023; 20. [PMID: 36645915 DOI: 10.1088/1741-2552/acb386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 01/16/2023] [Indexed: 01/18/2023]
Abstract
Objective. Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively.Approach.To explore action differences, this study proposes a novel paradigm named as action observation based delayed matching posture task. Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference.Main results.Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p< 0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28μV of grasping, 5.64μV and 5.25μV higher than the cutting and the writing, respectively (p< 0.05).Significance.The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, 300132 Tianjin, People's Republic of China
| | - Haoxin Zuo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, 300132 Tianjin, People's Republic of China
| | - Huihui Zhou
- Peng Cheng Laboratory, 518000 Guangdong, People's Republic of China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, 300132 Tianjin, People's Republic of China
| | - Enming Qi
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, 300132 Tianjin, People's Republic of China.,Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, 300132 Tianjin, People's Republic of China
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Hu H, Yue K, Guo M, Lu K, Liu Y. Subject Separation Network for Reducing Calibration Time of MI-Based BCI. Brain Sci 2023; 13:brainsci13020221. [PMID: 36831764 PMCID: PMC9954620 DOI: 10.3390/brainsci13020221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/11/2023] [Accepted: 01/20/2023] [Indexed: 02/03/2023] Open
Abstract
Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.
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Affiliation(s)
- Haochen Hu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Kang Yue
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Institute of Software, Chinese Academy of Sciences, Beijing 100045, China
| | - Mei Guo
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Kai Lu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Yue Liu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
- Correspondence:
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6
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Kasahara K, DaSalla CS, Honda M, Hanakawa T. Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans. Commun Biol 2022; 5:712. [PMID: 35842523 PMCID: PMC9288463 DOI: 10.1038/s42003-022-03665-6] [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: 11/05/2021] [Accepted: 06/30/2022] [Indexed: 11/09/2022] Open
Abstract
Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology. Simultaneous fMRI-EEG in 26 healthy participants indicate that the basal ganglia cortical network and the neurofeedback control network play different roles in self-regulation, providing further insight into the neural correlates for brain-machine interface control and feedback.
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Affiliation(s)
- Kazumi Kasahara
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan.,Department of Functional Brain Research, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan.,Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, 305-8566, Japan
| | - Charles S DaSalla
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan.,Department of Functional Brain Research, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan
| | - Manabu Honda
- Department of Functional Brain Research, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan. .,Department of Functional Brain Research, National Center of Neurology and Psychiatry, Tokyo, 187-8551, Japan. .,Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan.
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7
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Yang W, Zhang X, Li Z, Zhang Q, Xue C, Huai Y. The Effect of Brain–Computer Interface Training on Rehabilitation of Upper Limb Dysfunction After Stroke: A Meta-Analysis of Randomized Controlled Trials. Front Neurosci 2022; 15:766879. [PMID: 35197817 PMCID: PMC8859107 DOI: 10.3389/fnins.2021.766879] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background Upper limb motor dysfunction caused by stroke greatly affects the daily life of patients, significantly reduces their quality of life, and places serious burdens on society. As an emerging rehabilitation training method, brain–computer interface (BCI)–based training can provide closed-loop rehabilitation and is currently being applied to the restoration of upper limb function following stroke. However, because of the differences in the type of experimental clinical research, the quality of the literature varies greatly, and debate around the efficacy of BCI for the rehabilitation of upper limb dysfunction after stroke has continued. Objective We aimed to provide medical evidence-based support for BCI in the treatment of upper limb dysfunction after stroke by conducting a meta-analysis of relevant clinical studies. Methods The search terms used to retrieve related articles included “brain-computer interface,” “stroke,” and “upper extremity.” A total of 13 randomized controlled trials involving 258 participants were retrieved from five databases (PubMed, Cochrane Library, Science Direct, MEDLINE, and Web of Science), and RevMan 5.3 was used for data analysis. Results The total effect size for BCI training on upper limb motor function of post-stroke patients was 0.56 (95% CI: 0.29–0.83). Subgroup analysis indicated that the standard mean differences of BCI training on upper limb motor function of subacute stroke patients and chronic stroke patients were 1.10 (95% CI: 0.20–2.01) and 0.51 (95% CI: 0.09–0.92), respectively (p = 0.24). Conclusion Brain–computer interface training was shown to be effective in promoting upper limb motor function recovery in post-stroke patients, and the effect size was moderate.
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Rezaei K, Nami M, Sinaei E, Bagheri Z, Kordi Yoosefinejad A. A Comparison between Effects of Neurofeedback and Balance Exercise on Balance of Healthy Older Adults. J Biomed Phys Eng 2021; 11:713-722. [PMID: 34904068 PMCID: PMC8649161 DOI: 10.31661/jbpe.v0i0.1203] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 07/10/2019] [Indexed: 11/18/2022]
Abstract
Background: Balance ability is a crucial component of independent daily activities among the older adultss. Balance impairment is one of the major risk factors for falls and related complications. Objective: The present study aims to investigate and compare the effect of neurofeedback training and balance training on balance and fall risk among older adults. Material and Methods: In this randomized controlled trial, a total of 48 older adults aged more than 65 years were recruited and randomly assigned into two groups, neurofeedback group (n=24)
and balance exercise group (n=24). Prior to the intervention, the static balance, dynamic balance, and fall risk were measured using Biodex D balance system and Fullerton Advanced
Balance scale. Subjects in neurofeedback group received neurofeedback training for 12 sessions of 30-min, every other day. Moreover, subjects in balance exercise group
received balance training for four weeks in 12 sessions (45-minute) every other day. After the intervention, balance measurements were repeated in both groups. The significance level
was set at p<0.05. Results: Static balance and dynamic balance were shown to significantly improve, after the interventions (p<0.001). Furthermore, fall risk was significantly reduced, after the trial (p<0.001).
In addition, the therapeutic effect of neurofeedback training was not less significant than exercises on balance in the older adults (p<0.001). Conclusion: The findings suggest that both neurofeedback training and balance training improved balance ability among the older adults. Results also show the therapeutic effect of neurofeedback
training on balance in older people. However, further research is required to accurately investigate the long-term effects of these two treatment methods among the older adults.
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Affiliation(s)
- Katayoon Rezaei
- PhD, Department of Physical Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- PhD, Rehabilitation Sciences Research center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Nami
- PhD, Neuroscience Laboratory (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Science and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- PhD, DANA Brain Health Institute, Iranian Neuroscience Society, Fars Chapter, Shiraz, Iran
- PhD, Academy of Health, Senses Cultural Foundation, Sacramento, CA, USA
| | - Ehsan Sinaei
- MSc, Department of Physical therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- MSc, Rehabilitation Sciences Research center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Zahra Bagheri
- PhD, Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Amin Kordi Yoosefinejad
- PhD, Department of Physical Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- PhD, Rehabilitation Sciences Research center, Shiraz University of Medical Sciences, Shiraz, Iran
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Neurofeedback for cognitive enhancement and intervention and brain plasticity. Rev Neurol (Paris) 2021; 177:1133-1144. [PMID: 34674879 DOI: 10.1016/j.neurol.2021.08.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 08/27/2021] [Indexed: 12/18/2022]
Abstract
In recent years, neurofeedback has been used as a cognitive training tool to improve brain functions for clinical or recreational purposes. It is based on providing participants with feedback about their brain activity and training them to control it, initiating directional changes. The overarching hypothesis behind this method is that this control results in an enhancement of the cognitive abilities associated with this brain activity, and triggers specific structural and functional changes in the brain, promoted by learning and neuronal plasticity effects. Here, we review the general methodological principles behind neurofeedback and we describe its behavioural benefits in clinical and experimental contexts. We review the non-specific effects of neurofeedback on the reinforcement learning striato-frontal networks as well as the more specific changes in the cortical networks on which the neurofeedback control is exerted. Last, we analyse the current challenges faces by neurofeedback studies, including the quantification of the temporal dynamics of neurofeedback effects, the generalisation of its behavioural outcomes to everyday life situations, the design of appropriate controls to disambiguate placebo from true neurofeedback effects and the development of more advanced cortical signal processing to achieve a finer-grained real-time modelling of cognitive functions.
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10
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Zhou Q, Lin J, Yao L, Wang Y, Han Y, Xu K. Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects. Front Hum Neurosci 2021; 15:701091. [PMID: 34483866 PMCID: PMC8414415 DOI: 10.3389/fnhum.2021.701091] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/15/2021] [Indexed: 11/23/2022] Open
Abstract
One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.
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Affiliation(s)
- Qing Zhou
- Zhejiang Lab, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Jiafan Lin
- Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Lin Yao
- Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.,The College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yueming Wang
- Zhejiang Lab, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.,Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.,The College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Yan Han
- Zhejiang Key Laboratory of Neuroelectronics and Brain Computer Interface Technology, Hangzhou, China
| | - Kedi Xu
- Zhejiang Lab, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.,Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.,The College of Computer Science and Technology, Zhejiang University, Hangzhou, China
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11
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A Multivariate Randomized Controlled Experiment about the Effects of Mindfulness Priming on EEG Neurofeedback Self-Regulation Serious Games. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Neurofeedback training (NFT) is a technique often proposed to train brain activity SR with promising results. However, some criticism has been raised due to the lack of evaluation, reliability, and validation of its learning effects. The current work evaluates the hypothesis that SR learning may be improved by priming the subject before NFT with guided mindfulness meditation (MM). The proposed framework was tested in a two-way parallel-group randomized controlled intervention with a single session alpha NFT, in a simplistic serious game design. Sixty-two healthy naïve subjects, aged between 18 and 43 years, were divided into MM priming and no-priming groups. Although both the EG and CG successfully attained the up-regulation of alpha rhythms (F(1,59) = 20.67, p < 0.001, ηp2 = 0.26), the EG showed a significantly enhanced ability (t(29) = 4.38, p < 0.001) to control brain activity, compared to the CG (t(29) = 1.18, p > 0.1). Furthermore, EG superior performance on NFT seems to be explained by the subject’s lack of awareness at pre-intervention, less vigour at post-intervention, increased task engagement, and a relaxed non-judgemental attitude towards the NFT tasks. This study is a preliminary validation of the proposed assisted priming framework, advancing some implicit and explicit metrics about its efficacy on NFT performance, and a promising tool for improving naïve “users” self-regulation ability.
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12
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Abstract
Abstract
Neurofeedback (NF) is a versatile non-invasive neuromodulation technique. In combination with motor imagery (MI), NF has considerable potential for enhancing motor performance or supplementing motor rehabilitation. However, not all users achieve reliable NF control. While research has focused on various brain signal properties and the optimisation of signal processing to solve this issue, the impact of context, i.e. the conditions in which NF motor tasks occur, is comparatively unknown. We review current research on the impact of context on MI NF and related motor domains. We identify long-term factors that act at the level of the individual or of the intervention, and short-term factors, with levels before/after and during a session. The reviewed literature indicates that context plays a significant role. We propose considering context factors as well as within-level and across-level interactions when studying MI NF.
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13
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Trambaiolli LR, Tiwari A, Falk TH. Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges. FRONTIERS IN NEUROERGONOMICS 2021; 2:678981. [PMID: 38235228 PMCID: PMC10790905 DOI: 10.3389/fnrgo.2021.678981] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/28/2021] [Indexed: 01/19/2024]
Abstract
Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of "in-the-wild" protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.
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Affiliation(s)
- Lucas R. Trambaiolli
- Basic Neuroscience Division, McLean Hospital–Harvard Medical School, Belmont, MA, United States
| | - Abhishek Tiwari
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montreal, QC, Canada
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Kleih-Dahms SC, Botrel L, Kübler A. The influence of motivation and emotion on sensorimotor rhythm-based brain-computer interface performance. Psychophysiology 2021; 58:e13832. [PMID: 33945156 DOI: 10.1111/psyp.13832] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 04/01/2021] [Accepted: 04/06/2021] [Indexed: 01/20/2023]
Abstract
While decades of research have investigated and technically improved brain-computer interface (BCI)-controlled applications, relatively little is known about the psychological aspects of brain-computer interfacing. In 35 healthy students, we investigated whether extrinsic motivation manipulated via monetary reward and emotional state manipulated via video and music would influence behavioral and psychophysiological measures of performance with a sensorimotor rhythm (SMR)-based BCI. We found increased task-related brain activity in extrinsically motivated (rewarded) as compared with nonmotivated participants but no clear effect of emotional state manipulation. Our experiment investigated the short-term effect of motivation and emotion manipulation in a group of young healthy subjects, and thus, the significance for patients in the locked-in state, who may be in need of a BCI, remains to be investigated.
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Affiliation(s)
| | - Loic Botrel
- Institute of Psychology, University of Würzburg, Würzburg, Germany
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg, Würzburg, Germany
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15
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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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16
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Vidaurre C, Haufe S, Jorajuría T, Müller KR, Nikulin VV. Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance. Front Neurosci 2021; 14:575081. [PMID: 33390877 PMCID: PMC7775663 DOI: 10.3389/fnins.2020.575081] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/16/2020] [Indexed: 12/29/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining “good” and “poor” BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
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Affiliation(s)
- Carmen Vidaurre
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Tania Jorajuría
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - Klaus-Robert Müller
- Department of Machine Learning, Berlin University of Technology, Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul, South Korea.,Max Planck Institute for Informatics, Saarbrücken, Germany.,Google Research, Brain Team, Berlin, Germany
| | - Vadim V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Center for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
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Zhao Z, Yao S, Zweerings J, Zhou X, Zhou F, Kendrick KM, Chen H, Mathiak K, Becker B. Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions. Hum Brain Mapp 2021; 42:1879-1887. [PMID: 33400306 PMCID: PMC7978128 DOI: 10.1002/hbm.25336] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/17/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
Real-time fMRI guided neurofeedback training has gained increasing interest as a noninvasive brain regulation technique with the potential to modulate functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI data sets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three data sets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning this finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.
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Affiliation(s)
- Zhiying Zhao
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jana Zweerings
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Xinqi Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Klaus Mathiak
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
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18
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Reichert C, Dürschmid S, Bartsch MV, Hopf JM, Heinze HJ, Hinrichs H. Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface. J Neural Eng 2020; 17:056012. [PMID: 32906103 DOI: 10.1088/1741-2552/abb692] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE One of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts. APPROACH We present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component-a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure. MAIN RESULTS Despite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks. SIGNIFICANCE Our method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.
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Affiliation(s)
- Christoph Reichert
- Leibniz Institute for Neurobiology, Magdeburg, Germany. Forschungscampus STIMULATE, Magdeburg, Germany. Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany
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Khodakarami Z, Firoozabadi M. Psychological, Neurophysiological, and Mental Factors Associated With Gamma-Enhancing Neurofeedback Success. Basic Clin Neurosci 2020; 11:701-714. [PMID: 33643562 PMCID: PMC7878062 DOI: 10.32598/bcn.11.5.1878.1] [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: 05/27/2019] [Revised: 06/10/2019] [Accepted: 10/02/2020] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION Regarding the neurofeedback training process, previous studies indicate that 10%-50% of subjects cannot gain control over their brain activity even after repeated training sessions. This study is conducted to overcome this problem by investigating inter-individual differences in neurofeedback learning to propose some predictors for the trainability of subjects. METHODS Eight healthy female students took part in 8 (electroencephalography) EEG neurofeedback training sessions for enhancing EEG gamma power at the Oz channel. We studied participants' preexisting fluid intelligence and EEG frequency sub-bands' power during 2-min eyes-closed rest and a cognitive task as psychological and neurophysiological factors, concerning neurofeedback learning performance. We also assessed the self-reports of participants about mental strategies used by them during neurofeedback to identify the most effective successful strategies. RESULTS The results revealed that a significant percentage of individuals (25% in this study) cannot learn how to control their brain gamma activity using neurofeedback. Our findings suggest that fluid intelligence, gamma power during a cognitive task, and alpha power at rest can predict gamma-enhancing neurofeedback performance of individuals. Based on our study, neurofeedback learning is a form of implicit learning. We also found that learning without a user's mental efforts to find out successful mental strategies, in other words, unconscious learning, lead to more success in gamma-enhancing neurofeedback. CONCLUSION Our results may improve gamma neurofeedback efficacy for further clinical usage and studies by giving insight about both non-trainable individuals and effective mental strategies.
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Affiliation(s)
- Zeynab Khodakarami
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mohammad Firoozabadi
- Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere. Neuroimage 2020; 223:117298. [PMID: 32828924 DOI: 10.1016/j.neuroimage.2020.117298] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/04/2020] [Accepted: 08/16/2020] [Indexed: 12/26/2022] Open
Abstract
Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.
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21
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Stiso J, Corsi MC, Vettel JM, Garcia J, Pasqualetti F, De Vico Fallani F, Lucas TH, Bassett DS. Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior. J Neural Eng 2020; 17:046018. [PMID: 32369802 PMCID: PMC7734596 DOI: 10.1088/1741-2552/ab9064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. APPROACH Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression. MAIN RESULTS We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention. SIGNIFICANCE The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.
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Affiliation(s)
- Jennifer Stiso
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marie-Constance Corsi
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Jean M. Vettel
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
- Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Javier Garcia
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Human Research & Engineering Directorate, US CCDC Army Research Laboratory, Aberdeen, MD, USA
| | - Fabio Pasqualetti
- Department of Mechanical Engineering, University of California, Riverside, CA 92521
| | - Fabrizio De Vico Fallani
- Inria Paris, Aramis project-team, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Epinire, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Universit, F-75013, Paris, France
| | - Timothy H. Lucas
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- The Santa Fe Institute, Santa Fe, NM 87501, USA
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Use of Both Eyes-Open and Eyes-Closed Resting States May Yield a More Robust Predictor of Motor Imagery BCI Performance. ELECTRONICS 2020. [DOI: 10.3390/electronics9040690] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Motor-imagery brain-computer interface (MI-BCI) is a technique that manipulates external machines using brain activities, and is highly useful to amyotrophic lateral sclerosis patients who cannot move their limbs. However, it is reported that approximately 15–30% of users cannot modulate their brain signals, which results in the inability to operate motor imagery BCI systems. Thus, advance prediction of BCI performance has drawn researchers’ attention, and some predictors have been proposed using the alpha band’s power, as well as other spectral bands’ powers, or spectral entropy from resting state electroencephalography (EEG). However, these predictors rely on a single state alone, such as the eyes-closed or eyes-open state; thus, they may often be less stable or unable to explain inter-/intra-subject variability. In this work, a modified predictor of MI-BCI performance that considered both brain states (eyes-open and eyes-closed resting states) was investigated with 41 online MI-BCI session datasets acquired from 15 subjects. The results showed that our proposed predictor and online MI-BCI classification accuracy were positively and highly significantly correlated (r = 0.71, p < 0.1 × 10 − 7 ), which indicates that the use of multiple brain states may yield a more robust predictor than the use of a single state alone.
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Kober SE, Grössinger D, Wood G. Effects of Motor Imagery and Visual Neurofeedback on Activation in the Swallowing Network: A Real-Time fMRI Study. Dysphagia 2019; 34:879-895. [PMID: 30771088 PMCID: PMC6825652 DOI: 10.1007/s00455-019-09985-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/25/2019] [Indexed: 12/01/2022]
Abstract
Motor imagery of movements is used as mental strategy in neurofeedback applications to gain voluntary control over activity in motor areas of the brain. In the present functional magnetic resonance imaging (fMRI) study, we first addressed the question whether motor imagery and execution of swallowing activate comparable brain areas, which has been already proven for hand and foot movements. Prior near-infrared spectroscopy (NIRS) studies provide evidence that this is the case in the outer layer of the cortex. With the present fMRI study, we want to expand these prior NIRS findings to the whole brain. Second, we used motor imagery of swallowing as mental strategy during visual neurofeedback to investigate whether one can learn to modulate voluntarily activity in brain regions, which are associated with active swallowing, using real-time fMRI. Eleven healthy adults performed one offline session, in which they executed swallowing movements and imagined swallowing on command during fMRI scanning. Based on this functional localizer task, we identified brain areas active during both tasks and defined individually regions for feedback. During the second session, participants performed two real-time fMRI neurofeedback runs (each run comprised 10 motor imagery trials), in which they should increase voluntarily the activity in the left precentral gyrus by means of motor imagery of swallowing while receiving visual feedback (the visual feedback depicted one's own fMRI signal changes in real-time). Motor execution and imagery of swallowing activated a comparable network of brain areas including the bilateral pre- and postcentral gyrus, inferior frontal gyrus, basal ganglia, insula, SMA, and the cerebellum compared to a resting condition. During neurofeedback training, participants were able to increase the activity in the feedback region (left lateral precentral gyrus) but also in other brain regions, which are generally active during swallowing, compared to the motor imagery offline task. Our results indicate that motor imagery of swallowing is an adequate mental strategy to activate the swallowing network of the whole brain, which might be useful for future treatments of swallowing disorders.
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Affiliation(s)
- Silvia Erika Kober
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Doris Grössinger
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
| | - Guilherme Wood
- Institute of Psychology, University of Graz, Universitaetsplatz 2/III, 8010 Graz, Austria
- BioTechMed-Graz, Graz, Austria
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Lubianiker N, Goldway N, Fruchtman-Steinbok T, Paret C, Keynan JN, Singer N, Cohen A, Kadosh KC, Linden DEJ, Hendler T. Process-based framework for precise neuromodulation. Nat Hum Behav 2019; 3:436-445. [DOI: 10.1038/s41562-019-0573-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 03/05/2019] [Indexed: 12/20/2022]
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25
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Meekes J, Debener S, Zich C, Bleichner MG, Kranczioch C. Does Fractional Anisotropy Predict Motor Imagery Neurofeedback Performance in Healthy Older Adults? Front Hum Neurosci 2019; 13:69. [PMID: 30873015 PMCID: PMC6403184 DOI: 10.3389/fnhum.2019.00069] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 02/11/2019] [Indexed: 01/02/2023] Open
Abstract
Motor imagery neurofeedback training has been proposed as a potential add-on therapy for motor impairment after stroke, but not everyone benefits from it. Previous work has used white matter integrity to predict motor imagery neurofeedback aptitude in healthy young adults. We set out to test this approach with motor imagery neurofeedback that is closer to that used for stroke rehabilitation and in a sample whose age is closer to that of typical stroke patients. Using shrinkage linear discriminant analysis with fractional anisotropy values in 48 white matter regions as predictors, we predicted whether each participant in a sample of 21 healthy older adults (48–77 years old) was a good or a bad performer with 84.8% accuracy. However, the regions used for prediction in our sample differed from those identified previously, and previously suggested regions did not yield significant prediction in our sample. Including demographic and cognitive variables which may correlate with motor imagery neurofeedback performance and white matter structure as candidate predictors revealed an association with age but also led to loss of statistical significance and somewhat poorer prediction accuracy (69.6%). Our results suggest cast doubt on the feasibility of predicting the benefit of motor imagery neurofeedback from fractional anisotropy. At the very least, such predictions should be based on data collected using the same paradigm and with subjects whose characteristics match those of the target case as closely as possible.
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Affiliation(s)
- Joost Meekes
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| | - Stefan Debener
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
| | - Catharina Zich
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Center for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Martin G Bleichner
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Cluster of Excellence Hearing4All, University of Oldenburg, Oldenburg, Germany
| | - Cornelia Kranczioch
- Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany.,Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
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EEG neurofeedback research: A fertile ground for psychiatry? Encephale 2019; 45:245-255. [PMID: 30885442 DOI: 10.1016/j.encep.2019.02.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 02/02/2019] [Accepted: 02/11/2019] [Indexed: 11/20/2022]
Abstract
The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human-computer interaction that enables subjects to train voluntarily and modify functional biomarkers that are related to a defined mental disorder. We investigate three kinds of factors related to this definition of neurofeedback. We focus this article on EEG NFB. The first part of the paper investigates neurophysiological factors underlying the brain mechanisms driving NFB training and learning to modify a functional biomarker voluntarily. Two kinds of neuroplasticity involved in neurofeedback are analyzed: Hebbian neuroplasticity, i.e. long-term modification of neural membrane excitability and/or synaptic potentiation, and homeostatic neuroplasticity, i.e. homeostasis attempts to stabilize network activity. The second part investigates psychophysiological factors related to the targeted biomarker. It is demonstrated that neurofeedback involves clearly defining which kind of relationship between EEG biomarkers and clinical dimensions (symptoms or cognitive processes) is to be targeted. A nomenclature of accurate EEG biomarkers is proposed in the form of a short EEG encyclopedia (EEGcopia). The third part investigates human-computer interaction factors for optimizing NFB training and learning during the closed loop interaction. A model is proposed to summarize the different features that should be controlled to optimize learning. The need for accurate and reliable metrics of training and learning in line with human-computer interaction is also emphasized, including targeted biomarkers and neuroplasticity. All these factors related to neurofeedback show that it can be considered as a fertile ground for innovative research in psychiatry.
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Sannelli C, Vidaurre C, Müller KR, Blankertz B. A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. PLoS One 2019; 14:e0207351. [PMID: 30682025 PMCID: PMC6347432 DOI: 10.1371/journal.pone.0207351] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 10/30/2018] [Indexed: 11/18/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) are inefficient for a non-negligible part of the population, estimated around 25%. To understand this phenomenon in Sensorimotor Rhythm (SMR) based BCIs, data from a large-scale screening study conducted on 80 novice participants with the Berlin BCI system and its standard machine-learning approach were investigated. Each participant performed one BCI session with resting state Encephalography, Motor Observation, Motor Execution and Motor Imagery recordings and 128 electrodes. A significant portion of the participants (40%) could not achieve BCI control (feedback performance > 70%). Based on the performance of the calibration and feedback runs, BCI users were stratified in three groups. Analyses directed to detect and elucidate the differences in the SMR activity of these groups were performed. Statistics on reactive frequencies, task prevalence and classification results are reported. Based on their SMR activity, also a systematic list of potential reasons leading to performance drops and thus hints for possible improvements of BCI experimental design are given. The categorization of BCI users has several advantages, allowing researchers 1) to select subjects for further analyses as well as for testing new BCI paradigms or algorithms, 2) to adopt a better subject-dependent training strategy and 3) easier comparisons between different studies.
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Affiliation(s)
- Claudia Sannelli
- Department of Neurotechnology, Technische Universität Berlin, Berlin, Germany
| | - Carmen Vidaurre
- Department of Machine Learning, Technische Universität Berlin, Berlin, Germany
- Department of Mathematics, Public University of Navarre, Pamplona, Spain
| | - Klaus-Robert Müller
- Department of Machine Learning, Technische Universität Berlin, Berlin, Germany
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Benjamin Blankertz
- Department of Neurotechnology, Technische Universität Berlin, Berlin, Germany
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28
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Darvishi S, Gharabaghi A, Ridding MC, Abbott D, Baumert M. Reaction Time Predicts Brain-Computer Interface Aptitude. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:2000311. [PMID: 30533323 PMCID: PMC6280922 DOI: 10.1109/jtehm.2018.2875985] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 11/28/2017] [Accepted: 09/22/2018] [Indexed: 01/24/2023]
Abstract
There is evidence that 15-30% of the general population cannot effectively operate brain-computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity. Thus, a simple and objective BCI performance predictor is desirable. Neurofeedback (NFB) training involves performing a cognitive task (motor imagery) instructed via sensory stimuli and re-adjusted through ongoing real-time feedback. A simple reaction time (SRT) test reflects the time required for a subject to respond to a defined stimulus. Thus, we postulated that individuals with shorter reaction times operate a BCI with rapidly updated feedback better than individuals with longer reaction times. Furthermore, we investigated how changing the feedback update interval (FUI), i.e., modification of the feedback provision frequency, affects the correlation between the SRT and BCI performance. Ten participants attended four NFB sessions with FUIs of 16, 24, 48, and 96 ms in a randomized order. We found that: 1) SRT is correlated with the BCI performance with FUIs of 16 and 96 ms; 2) good and poor performers elicit stronger ERDs and control BCIs more effectively (i.e., produced larger information transfer rates) with 16 and 96 ms FUIs, respectively. Our findings suggest that SRT may be used as a simple and objective surrogate for BCI aptitude with FUIs of 16 and 96 ms. It also implies that the FUI customization according to participants SRT measure may enhance the BCI performance.
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Affiliation(s)
- Sam Darvishi
- School of Electrical and Electronic EngineeringThe University of Adelaide SA 5005 Australia
| | - Alireza Gharabaghi
- Division of Functional and Restorative NeurosurgeryUniversity of Tübingen 72074 Tübingen Germany
| | - Michael C Ridding
- Robinson Research Institute, School of MedicineThe University of Adelaide SA 5005 Australia
| | - Derek Abbott
- School of Electrical and Electronic EngineeringThe University of Adelaide SA 5005 Australia
| | - Mathias Baumert
- School of Electrical and Electronic EngineeringThe University of Adelaide SA 5005 Australia
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Kober SE, Witte M, Grinschgl S, Neuper C, Wood G. Placebo hampers ability to self-regulate brain activity: A double-blind sham-controlled neurofeedback study. Neuroimage 2018; 181:797-806. [DOI: 10.1016/j.neuroimage.2018.07.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 07/11/2018] [Accepted: 07/11/2018] [Indexed: 01/03/2023] Open
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Taherian S, Davies TC. Caregiver and special education staff perspectives of a commercial brain-computer interface as access technology: a qualitative study. BRAIN-COMPUTER INTERFACES 2018. [DOI: 10.1080/2326263x.2018.1505191] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Sarvnaz Taherian
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - T. Claire Davies
- Faculty of Engineering and Applied Science, Queens University, Kingston, Ontario, Canada
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Trambaiolli LR, Biazoli CE, Cravo AM, Falk TH, Sato JR. Functional near-infrared spectroscopy-based affective neurofeedback: feedback effect, illiteracy phenomena, and whole-connectivity profiles. NEUROPHOTONICS 2018; 5:035009. [PMID: 30689679 PMCID: PMC6156400 DOI: 10.1117/1.nph.5.3.035009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 08/10/2018] [Indexed: 05/11/2023]
Abstract
Background: Affective neurofeedback constitutes a suitable approach to control abnormal neural activities associated with psychiatric disorders and might consequently relief symptom severity. However, different aspects of neurofeedback remain unclear, such as its neural basis, the performance variation, the feedback effect, among others. Aim: First, we aimed to propose a functional near-infrared spectroscopy (fNIRS)-based affective neurofeedback based on the self-regulation of frontal and occipital networks. Second, we evaluated three different feedback approaches on performance: real, fixed, and random feedback. Third, we investigated different demographic, psychological, and physiological predictors of performance. Approach: Thirty-three healthy participants performed a task whereby an amorphous figure changed its shape according to the elicited affect (positive or neutral). During the task, the participants randomly received three different feedback approaches: real feedback, with no change of the classifier output; fixed feedback, keeping the feedback figure unmodified; and random feedback, where the classifier output was multiplied by an arbitrary value, causing a feedback different than expected by the subject. Then, we applied a multivariate comparison of the whole-connectivity profiles according to the affective states and feedback approaches, as well as during a pretask resting-state block, to predict performance. Results: Participants were able to control this feedback system with 70.00 % ± 24.43 % ( p < 0.01 ) of performance during the real feedback trials. No significant differences were found when comparing the average performances of the feedback approaches. However, the whole functional connectivity profiles presented significant Mahalanobis distances ( p ≪ 0.001 ) when comparing both affective states and all feedback approaches. Finally, task performance was positively correlated to the pretask resting-state whole functional connectivity ( r = 0.512 , p = 0.009 ). Conclusions: Our results suggest that fNIRS might be a feasible tool to develop a neurofeedback system based on the self-regulation of affective networks. This finding enables future investigations using an fNIRS-based affective neurofeedback in psychiatric populations. Furthermore, functional connectivity profiles proved to be a good predictor of performance and suggested an increased effort to maintain task control in the presence of feedback distractors.
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Affiliation(s)
- Lucas R. Trambaiolli
- Universidade Federal do ABC, Mathematics, Computation and Cognition Center, Santo André, São Paulo, Brazil
- University of Quebec, Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, Montreal, Quebec, Canada
- Address all correspondence to: Lucas R. Trambaiolli, E-mail:
| | - Claudinei E. Biazoli
- Universidade Federal do ABC, Mathematics, Computation and Cognition Center, Santo André, São Paulo, Brazil
| | - André M. Cravo
- Universidade Federal do ABC, Mathematics, Computation and Cognition Center, Santo André, São Paulo, Brazil
| | - Tiago H. Falk
- University of Quebec, Institut National de la Recherche Scientifique, Centre Énergie, Matériaux, Télécommunications, Montreal, Quebec, Canada
| | - João R. Sato
- Universidade Federal do ABC, Mathematics, Computation and Cognition Center, Santo André, São Paulo, Brazil
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Azarpaikan A, Taheri Torbati H. Effect of somatosensory and neurofeedback training on balance in older healthy adults: a preliminary investigation. Aging Clin Exp Res 2018; 30:745-753. [PMID: 29063490 DOI: 10.1007/s40520-017-0835-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 09/17/2017] [Indexed: 10/18/2022]
Abstract
The aim of this study was to assess the effectiveness of balance training with somatosensory and neurofeedback training on dynamic and static balance in healthy, elderly adults. The sample group consisted of 45 healthy adults randomly assigned to one of the three test groups: somatosensory, neurofeedback, and a control. Individualization of the balance program started with pre-tests for static and dynamic balances. Each group had 15- and 30-min training sessions. All groups were tested for static (postural stability) and dynamic balances (Berg Balance Scale) in acquisition and transfer tests (fall risk of stability and timed up and go). Improvements in static and dynamic balances were assessed by somatosensory and neurofeedback groups and then compared with the control group. Results indicated significant improvements in static and dynamic balances in both test groups in the acquisition test. Results revealed a significant improvement in the transfer test in the neurofeedback and somatosensory groups, in static and dynamic conditions, respectively. The findings suggest that these methods of balance training had a significant influence on balance. Both the methods are appropriate to prevent falling in adults. Neurofeedback training helped the participants to learn static balance, while somatosensory training was effective on dynamic balance learning. Further research is needed to assess the effects of longer and discontinuous stimulation with somatosensory and neurofeedback training on balance in elderly adults.
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Alkoby O, Abu-Rmileh A, Shriki O, Todder D. Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning. Neuroscience 2018; 378:155-164. [DOI: 10.1016/j.neuroscience.2016.12.050] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2015] [Revised: 12/25/2016] [Accepted: 12/28/2016] [Indexed: 10/20/2022]
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35
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Diaz Hernandez L, Rieger K, Koenig T. Low Motivational Incongruence Predicts Successful EEG Resting-state Neurofeedback Performance in Healthy Adults. Neuroscience 2018; 378:146-154. [DOI: 10.1016/j.neuroscience.2016.12.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 12/02/2016] [Indexed: 11/29/2022]
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36
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Ekanayake J, Hutton C, Ridgway G, Scharnowski F, Weiskopf N, Rees G. Real-time decoding of covert attention in higher-order visual areas. Neuroimage 2018; 169:462-472. [PMID: 29247807 PMCID: PMC5864512 DOI: 10.1016/j.neuroimage.2017.12.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/06/2017] [Accepted: 12/09/2017] [Indexed: 12/21/2022] Open
Abstract
Brain-computer-interfaces (BCI) provide a means of using human brain activations to control devices for communication. Until now this has only been demonstrated in primary motor and sensory brain regions, using surgical implants or non-invasive neuroimaging techniques. Here, we provide proof-of-principle for the use of higher-order brain regions involved in complex cognitive processes such as attention. Using realtime fMRI, we implemented an online 'winner-takes-all approach' with quadrant-specific parameter estimates, to achieve single-block classification of brain activations. These were linked to the covert allocation of attention to real-world images presented at 4-quadrant locations. Accuracies in three target regions were significantly above chance, with individual decoding accuracies reaching upto 70%. By utilising higher order mental processes, 'cognitive BCIs' access varied and therefore more versatile information, potentially providing a platform for communication in patients who are unable to speak or move due to brain injury.
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Affiliation(s)
- Jinendra Ekanayake
- Wellcome Trust Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Institute of Cognitive Neuroscience, University College London, London, United Kingdom.
| | - Chloe Hutton
- Siemens Molecular Imaging, Oxford, United Kingdom
| | | | - Frank Scharnowski
- Psychiatric University Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057 Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057 Zürich, Switzerland
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Geraint Rees
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom; Institute of Cognitive Neuroscience, University College London, London, United Kingdom
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Korik A, Sosnik R, Siddique N, Coyle D. Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations. Front Neurosci 2018; 12:130. [PMID: 29615848 PMCID: PMC5869206 DOI: 10.3389/fnins.2018.00130] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Accepted: 02/19/2018] [Indexed: 12/03/2022] Open
Abstract
Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands. Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep). Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations. Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.
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Affiliation(s)
- Attila Korik
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Ronen Sosnik
- Hybrid BCI Lab, Holon Institute of Technology, Holon, Israel
| | - Nazmul Siddique
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
| | - Damien Coyle
- Intelligent Systems Research Centre, Ulster University, Derry, United Kingdom
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Shu X, Chen S, Yao L, Sheng X, Zhang D, Jiang N, Jia J, Zhu X. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients. Front Neurosci 2018; 12:93. [PMID: 29515363 PMCID: PMC5826359 DOI: 10.3389/fnins.2018.00093] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 02/05/2018] [Indexed: 11/13/2022] Open
Abstract
Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732, p < 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (r = 0.641, p < 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.
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Affiliation(s)
- Xiaokang Shu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Shugeng Chen
- Department of Rehabilitation, Huashan Hospital, Shanghai, China
| | - Lin Yao
- Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Dingguo Zhang
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Ning Jiang
- Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Jie Jia
- Department of Rehabilitation, Huashan Hospital, Shanghai, China
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
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Ahn M, Cho H, Ahn S, Jun SC. User's Self-Prediction of Performance in Motor Imagery Brain-Computer Interface. Front Hum Neurosci 2018; 12:59. [PMID: 29497370 PMCID: PMC5818431 DOI: 10.3389/fnhum.2018.00059] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 01/31/2018] [Indexed: 11/28/2022] Open
Abstract
Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user’s sense of the motor imagery process and directly estimate MI-BCI performance through the user’s self-prediction are lacking. In this study, we first test each user’s self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject’s self-prediction of MI-BCI performance. The subjects’ performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.
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Affiliation(s)
- Minkyu Ahn
- School of Computer Science and Electrical Engineering, Handong Global University, Pohang, South Korea
| | - Hohyun Cho
- Wadsworth Center, New York State Department of Health, Albany, NY, United States
| | - Sangtae Ahn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Sung C Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
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Stefano Filho CA, Attux R, Castellano G. EEG sensorimotor rhythms' variation and functional connectivity measures during motor imagery: linear relations and classification approaches. PeerJ 2017; 5:e3983. [PMID: 29134143 PMCID: PMC5681853 DOI: 10.7717/peerj.3983] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 10/12/2017] [Indexed: 11/21/2022] Open
Abstract
Hands motor imagery (MI) has been reported to alter synchronization patterns amongst neurons, yielding variations in the mu and beta bands' power spectral density (PSD) of the electroencephalography (EEG) signal. These alterations have been used in the field of brain-computer interfaces (BCI), in an attempt to assign distinct MI tasks to commands of such a system. Recent studies have highlighted that information may be missing if knowledge about brain functional connectivity is not considered. In this work, we modeled the brain as a graph in which each EEG electrode represents a node. Our goal was to understand if there exists any linear correlation between variations in the synchronization patterns-that is, variations in the PSD of mu and beta bands-induced by MI and alterations in the corresponding functional networks. Moreover, we (1) explored the feasibility of using functional connectivity parameters as features for a classifier in the context of an MI-BCI; (2) investigated three different types of feature selection (FS) techniques; and (3) compared our approach to a more traditional method using the signal PSD as classifier inputs. Ten healthy subjects participated in this study. We observed significant correlations (p < 0.05) with values ranging from 0.4 to 0.9 between PSD variations and functional network alterations for some electrodes, prominently in the beta band. The PSD method performed better for data classification, with mean accuracies of (90 ± 8)% and (87 ± 7)% for the mu and beta band, respectively, versus (83 ± 8)% and (83 ± 7)% for the same bands for the graph method. Moreover, the number of features for the graph method was considerably larger. However, results for both methods were relatively close, and even overlapped when the uncertainties of the accuracy rates were considered. Further investigation regarding a careful exploration of other graph metrics may provide better alternatives.
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Affiliation(s)
- Carlos A. Stefano Filho
- Neurophysics group, “Gleb Wataghing” Institute of Physics, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Brazil
| | - Romis Attux
- Brazilian Institute of Neuroscience and Neurotechnology, Brazil
- Department of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil
| | - Gabriela Castellano
- Neurophysics group, “Gleb Wataghing” Institute of Physics, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Brazil
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Gong J, Luo C, Chang X, Zhang R, Klugah-Brown B, Guo L, Xu P, Yao D. White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals. Brain Topogr 2017; 30:797-809. [PMID: 28785973 DOI: 10.1007/s10548-017-0581-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 07/27/2017] [Indexed: 12/01/2022]
Abstract
The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.
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Affiliation(s)
- Jinnan Gong
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Cheng Luo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xuebin Chang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Rui Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Klugah-Brown
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lanjin Guo
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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42
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Oblak EF, Lewis-Peacock JA, Sulzer JS. Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment. PLoS Comput Biol 2017; 13:e1005681. [PMID: 28753639 PMCID: PMC5550007 DOI: 10.1371/journal.pcbi.1005681] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 08/09/2017] [Accepted: 07/13/2017] [Indexed: 01/15/2023] Open
Abstract
Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different self-regulation mechanisms prefer different feedback timings, and that these factors can be effectively explored and optimized via simulation prior to deployment in the MRI scanner. By repeatedly stimulating fine-grained patterns of neural activity, it is possible to manipulate behaviors associated with these patterns. While millimeter-scale patterns cannot yet be targeted with noninvasive brain stimulation, some people can learn to self-stimulate these activity patterns if they receive real-time feedback of their own recorded brain activity through a procedure known as fMRI neurofeedback. Other ‘non-responders’ are, for reasons unknown, unable to learn how to self-regulate these patterns. Here, we investigate how the properties of the fMRI signal, feedback timing, and self-regulation strategies may lead to this non-responder effect. The signal recorded by fMRI is related to blood flow in the brain and can be delayed by up to six seconds relative to underlying neural activity, which makes it difficult to learn. Because experiments in the MRI scanner are costly and time-consuming, we created a simulated neurofeedback environment to compare continuous versus intermittent feedback timing and cognitive versus automatic self-regulation strategies. In a cognitive experiment with human participants playing a simple game with the simulated neurofeedback signal, we found continuous feedback led to faster learning. However, in a computer model of automatic reward-based learning, we found that intermittent feedback was more reliable. These results will help improve future fMRI neurofeedback experiments and treatments by improving the efficacy of neurofeedback training procedures.
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Affiliation(s)
- Ethan F. Oblak
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, USA
- * E-mail:
| | - Jarrod A. Lewis-Peacock
- Department of Psychology, The University of Texas at Austin, Austin, Texas, USA
- Institute for Neuroscience, The University of Texas at Austin, Austin, Texas, USA
| | - James S. Sulzer
- Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, USA
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43
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Mangia AL, Ursino M, Lannocca M, Cappello A. Transcallosal Inhibition during Motor Imagery: Analysis of a Neural Mass Model. Front Comput Neurosci 2017; 11:57. [PMID: 28713259 PMCID: PMC5491977 DOI: 10.3389/fncom.2017.00057] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 06/12/2017] [Indexed: 11/13/2022] Open
Abstract
The EEG rhythmic activities of the somato-sensory cortex reveal event-related desynchronization (ERD) or event-related synchronization (ERS) in beta band (14–30 Hz) as subjects perform certain tasks or react to specific stimuli. Data reported for imagination of movement support the hypothesis that activation of one sensorimotor area (SMA) can be accompanied by deactivation of the other. In order to improve our understanding of beta ERD/ERS generation, two neural mass models (NMM) of a cortical column taken from Wendling et al. (2002) were interconnected to simulate the transmission of information from one cortex to the other. The results show that the excitation of one cortex leads to inhibition of the other and vice versa, enforcing the Theory of Inhibition. This behavior strongly depends on the initial working point (WP) of the neural populations (between the linear and the upper saturation region of a sigmoidal function) and on how the cortical activation or deactivation can move the WP in the upper saturation region ERD or in the linear region ERS, respectively.
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Affiliation(s)
- Anna L Mangia
- Department of Electrical, Electronic and Information Engineering, University of BolognaCesena, Italy
| | - Mauro Ursino
- Department of Electrical, Electronic and Information Engineering, University of BolognaCesena, Italy
| | - Maurizio Lannocca
- Department of Electrical, Electronic and Information Engineering, University of BolognaCesena, Italy
| | - Angelo Cappello
- Department of Electrical, Electronic and Information Engineering, University of BolognaCesena, Italy
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44
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Kober SE, Witte M, Ninaus M, Koschutnig K, Wiesen D, Zaiser G, Neuper C, Wood G. Ability to Gain Control Over One's Own Brain Activity and its Relation to Spiritual Practice: A Multimodal Imaging Study. Front Hum Neurosci 2017; 11:271. [PMID: 28596726 PMCID: PMC5442174 DOI: 10.3389/fnhum.2017.00271] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 05/08/2017] [Indexed: 01/05/2023] Open
Abstract
Spiritual practice, such as prayer or meditation, is associated with focusing attention on internal states and self-awareness processes. As these cognitive control mechanisms presumably are also important for neurofeedback (NF), we investigated whether people who pray frequently (N = 20) show a higher ability of self-control over their own brain activity compared to a control group of individuals who rarely pray (N = 20). All participants underwent structural magnetic resonance imaging (MRI) and one session of sensorimotor rhythm (SMR, 12–15 Hz) based NF training. Individuals who reported a high frequency of prayer showed improved NF performance compared to individuals who reported a low frequency of prayer. The individual ability to control one’s own brain activity was related to volumetric aspects of the brain. In the low frequency of prayer group, gray matter volumes in the right insula and inferior frontal gyrus were positively associated with NF performance, supporting prior findings that more general self-control networks are involved in successful NF learning. In contrast, participants who prayed regularly showed a negative association between gray matter volume in the left medial orbitofrontal cortex (Brodmann’s area (BA) 10) and NF performance. Due to their regular spiritual practice, they might have been more skillful in gating incoming information provided by the NF system and avoiding task-irrelevant thoughts.
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Affiliation(s)
- Silvia E Kober
- Department of Psychology, University of GrazGraz, Austria.,BioTechMed-GrazGraz, Austria
| | - Matthias Witte
- Department of Psychology, University of GrazGraz, Austria
| | - Manuel Ninaus
- Leibniz-Institut für WissensmedienTuebingen, Germany.,LEAD Graduate School and Research Network, Eberhard Karls University TuebingenTuebingen, Germany
| | - Karl Koschutnig
- Department of Psychology, University of GrazGraz, Austria.,BioTechMed-GrazGraz, Austria
| | - Daniel Wiesen
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of TuebingenTuebingen, Germany
| | - Gabriela Zaiser
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of TuebingenTuebingen, Germany
| | - Christa Neuper
- Department of Psychology, University of GrazGraz, Austria.,BioTechMed-GrazGraz, Austria.,Laboratory of Brain-Computer Interfaces, Institute for Neural Engineering, Graz University of TechnologyGraz, Austria
| | - Guilherme Wood
- Department of Psychology, University of GrazGraz, Austria.,BioTechMed-GrazGraz, Austria
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45
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Enriquez-Geppert S, Huster RJ, Herrmann CS. EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Front Hum Neurosci 2017; 11:51. [PMID: 28275344 PMCID: PMC5319996 DOI: 10.3389/fnhum.2017.00051] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/23/2017] [Indexed: 01/02/2023] Open
Abstract
Neurofeedback is attracting renewed interest as a method to self-regulate one’s own brain activity to directly alter the underlying neural mechanisms of cognition and behavior. It not only promises new avenues as a method for cognitive enhancement in healthy subjects, but also as a therapeutic tool. In the current article, we present a review tutorial discussing key aspects relevant to the development of electroencephalography (EEG) neurofeedback studies. In addition, the putative mechanisms underlying neurofeedback learning are considered. We highlight both aspects relevant for the practical application of neurofeedback as well as rather theoretical considerations related to the development of new generation protocols. Important characteristics regarding the set-up of a neurofeedback protocol are outlined in a step-by-step way. All these practical and theoretical considerations are illustrated based on a protocol and results of a frontal-midline theta up-regulation training for the improvement of executive functions. Not least, assessment criteria for the validation of neurofeedback studies as well as general guidelines for the evaluation of training efficacy are discussed.
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Affiliation(s)
- Stefanie Enriquez-Geppert
- Department of Clinical and Developmental Neuropsychology, Faculty of Behavioural and Social Sciences, University of Groningen Groningen, Netherlands
| | - René J Huster
- Department of Psychology, Faculty of Social Sciences, University of Oslo Oslo, Norway
| | - Christoph S Herrmann
- Experimental Psychology Laboratory, Department of Psychology, Faculty VI Medical and Health Sciences, University of Oldenburg Oldenburg, Germany
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46
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Saeedi S, Chavarriaga R, Millan JDR. Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance. IEEE Trans Neural Syst Rehabil Eng 2016; 25:380-391. [PMID: 28055886 DOI: 10.1109/tnsre.2016.2645681] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control approaches used for BCIs and we review some of the recent studies at the light of these approaches. We posit that the level of assistance provided to the BCI user should be adjusted in real time in order to enhance BCI reliability over time. This approach has not been extensively studied in the recent literature on BCIs. In addition, we investigate the effectiveness of providing online adaptive assistance in a motor-imagery BCI for a tetraplegic end-user with an incomplete locked-in syndrome in a longitudinal study lasting 11 months. First, we report a reliable estimation of the BCI performance (in terms of command delivery time) using only a window of 1 s in the beginning of trials (AUC ≈ 0.8 ). Second, we demonstrate how adaptive shared control can exploit the output of the performance estimator to adjust online the level of assistance in a BCI game by regulating its speed. In particular, online adaptive assistance was superior to a fixed condition in terms of success rate ( ). Remarkably, the results exhibited a stable performance over severalmonths without recalibration of the BCI classifier or the performance estimator.
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47
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Sitaram R, Ros T, Stoeckel L, Haller S, Scharnowski F, Lewis-Peacock J, Weiskopf N, Blefari ML, Rana M, Oblak E, Birbaumer N, Sulzer J. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci 2016; 18:86-100. [PMID: 28003656 DOI: 10.1038/nrn.2016.164] [Citation(s) in RCA: 527] [Impact Index Per Article: 65.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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48
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Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network. Neuroimage 2016; 134:475-485. [PMID: 27103137 DOI: 10.1016/j.neuroimage.2016.04.030] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/04/2016] [Accepted: 04/13/2016] [Indexed: 11/21/2022] Open
Abstract
Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.
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49
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Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep 2016; 6:24076. [PMID: 27052520 PMCID: PMC4823701 DOI: 10.1038/srep24076] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 03/15/2016] [Indexed: 11/08/2022] Open
Abstract
Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related to differences in MI ability. We assessed whether differences in kinesthetic and visual MI, in the behavioral accuracy of MI, and in electroencephalographic variables, were able to differentiate between high- versus low-aptitude BMI users. High-aptitude BMI users showed higher MI accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. Additionally, for the first time, we applied mental chronometry, a measure quantifying the degree to which imagined and executed movements share a similar temporal profile. We also identified enhanced lateralized μ-band oscillations over sensorimotor cortices during MI in high- versus low-aptitude BMI users. These findings reveal that subjective, behavioral, and EEG measurements of MI are intimately linked to BMI control. We propose that poor BMI control cannot be ascribed only to intrinsic limitations of EEG recordings and that specific questionnaires and mental chronometry can be used as predictors of BMI performance (without the need to record EEG activity).
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50
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Saeedi S, Chavarriaga R, Leeb R, Millan JDR. Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability. IEEE COMPUT INTELL M 2016. [DOI: 10.1109/mci.2015.2501550] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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