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Brain Science Advances 2020, 6(3): 224-241 https://doi.org/10.26599/BSA.2020.9050021
Review Article | Open Access | Issue | Published: 04 February 2021

Subject inefficiency phenomenon of motor imagery brain- computer interface: Influence factors and potential solutions

Show Author's Information Rui Zhang1,  Fali Li2,  Tao Zhang3,  Dezhong Yao1,2,  Peng Xu2( )
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
Science of School, Xihua University, Chengdu 610039, Sichuan, China
Keywords:
motor imagery brain-computer interface (MI-BCI), inefficient BCI user, EEG indicator, brain structure, transfer learning
Cite this article:
Zhang R, Li F, Zhang T, et al. Subject inefficiency phenomenon of motor imagery brain- computer interface: Influence factors and potential solutions. Brain Science Advances, 2020, 6(3): 224-241. https://doi.org/10.26599/BSA.2020.9050021
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Abstract Full text About this article

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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About this article

Subject inefficiency phenomenon of motor imagery brain- computer interface: Influence factors and potential solutions

Show Author's information Rui Zhang1, Fali Li2, Tao Zhang3, Dezhong Yao1,2, Peng Xu2( )
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
Science of School, Xihua University, Chengdu 610039, Sichuan, China

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.

Keywords: motor imagery brain-computer interface (MI-BCI), inefficient BCI user, EEG indicator, brain structure, transfer learning

References(100)

[1]
JJ Vidal. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973, 2(1): 157-180.
Google Scholar
[2]
JR Wolpaw, N Birbaumer, DJ McFarland, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002, 113(6): 767-791.
Google Scholar
[3]
M Akcakaya, B Peters, M Moghadamfalahi, et al. Noninvasive brain-computer interfaces for augmentative and alternative communication. IEEE Rev Biomed Eng. 2014, 7: 31-49.
Google Scholar
[4]
BJ Edelman, J Meng, D Suma, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot. 2019, 4(31): eaaw6844.
Google Scholar
[5]
A Biasiucci, R Leeb, I Iturrate, et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun. 2018, 9(1): 2421.
Google Scholar
[6]
JH Pan, QY Xie, PM Qin, et al. Prognosis for patients with cognitive motor dissociation identified by brain-computer interface. Brain. 2020, 143(4): 1177-1189.
Google Scholar
[7]
XG Chen, YJ Wang, M Nakanishi, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA. 2015, 112(44): E6058-E6067.
Google Scholar
[8]
SK Gao, YJ Wang, XR Gao, et al. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng. 2014, 61(5): 1436-1447.
Google Scholar
[9]
VJ Lawhern, AJ Solon, NR Waytowich, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng. 2018, 15(5): 056013.
Google Scholar
[10]
XY Zhu, PY Li, CB Li, et al. Separated channel convolutional neural network to realize the training free motor imagery BCI systems. Biomed Signal Process Control. 2019, 49: 396-403.
Google Scholar
[11]
F Marini, C Lee, J Wagner, et al. A comparative evaluation of signal quality between a research-grade and a wireless dry-electrode mobile EEG system. J Neural Eng. 2019, 16(5): 054001.
Google Scholar
[12]
J Jiang, EW Yin, CH Wang, et al. Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs. J Neural Eng. 2018, 15(4): 046025.
Google Scholar
[13]
S Hétu, M Grégoire, A Saimpont, et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev. 2013, 37(5): 930-949.
Google Scholar
[14]
G Pfurtscheller, FH Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999, 110(11): 1842-1857.
Google Scholar
[15]
C Sannelli, T Dickhaus, S Halder, et al. On optimal channel configurations for SMR-based brain- computer interfaces. Brain Topogr. 2010, 23(2): 186-193.
Google Scholar
[16]
J Müller-Gerking, G Pfurtscheller, H Flyvbjerg. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol. 1999, 110(5): 787-798.
Google Scholar
[17]
N Padfield, J Zabalza, HM Zhao, et al. EEG-based brain-computer interfaces using motor-imagery: techniques and challenges. Sensors. 2019, 19(6): E1423.
Google Scholar
[18]
S Saha, M Baumert. Intra- and inter-subject variability in EEG-based sensorimotor brain computer interface: a review. Front Comput Neurosci. 2019, 13: 87.
Google Scholar
[19]
F Lotte, L Bougrain, A Cichocki, et al. A review of classification algorithms for EEG-based brain- computer interfaces: a 10 year update. J Neural Eng. 2018, 15(3): 031005.
Google Scholar
[20]
C Vidaurre, M Kawanabe, P von Bünau, et al. Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE Trans Biomed Eng. 2011, 58(3): 587-597.
Google Scholar
[21]
R Zhang, P Xu, LJ Guo, et al. Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS One. 2013, 8(9): e74433.
Google Scholar
[22]
W Wu, Z Chen, XR Gao, et al. Probabilistic common spatial patterns for multichannel EEG analysis. IEEE T Pattern Anal. 2014, 37(3): 639-653.
Google Scholar
[23]
R Lorenz, J Pascual, B Blankertz, et al. Towards a holistic assessment of the user experience with hybrid BCIs. J Neural Eng. 2014, 11(3): 035007.
Google Scholar
[24]
MH Lee, OY Kwon, YJ Kim, et al. EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy. Gigascience. 2019, 8(5): giz002.
Google Scholar
[25]
C Vidaurre, B Blankertz. Towards a cure for BCI illiteracy. Brain Topogr. 2010, 23(2): 194-198.
Google Scholar
[26]
A Kübler, N Neumann, J Kaiser, et al. Brain- computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil. 2001, 82(11): 1533-1539.
Google Scholar
[27]
EM Hammer, T Kaufmann, SC Kleih, et al. Visuo- motor coordination ability predicts performance with brain-computer interfaces controlled by modulation of sensorimotor rhythms (SMR). Front Hum Neurosci. 2014, 8: 574.
Google Scholar
[28]
MC Thompson. Critiquing the concept of BCI illiteracy. Sci Eng Ethics. 2019, 25(4): 1217-1233.
Google Scholar
[29]
C Sannelli, C Vidaurre, KR Müller, et al. A large scale screening study with a SMR-based BCI: Categorization of BCI users and differences in their SMR activity. PLoS One. 2019, 14(1): e0207351.
Google Scholar
[30]
C Guger, G Edlinger, W Harkam, et al. How many people are able to operate an EEG-based brain- computer interface (BCI)? IEEE Trans Neural Syst Rehabil Eng. 2003, 11(2): 145-147.
Google Scholar
[31]
B Blankertz, C Sannelli, S Halder, et al. Neurophysiological predictor of SMR-based BCI performance. NeuroImage. 2010, 51(4): 1303-1309.
Google Scholar
[32]
R Zhang, P Xu, R Chen, et al. Predicting inter- session performance of SMR-based brain-computer interface using the spectral entropy of resting-state EEG. Brain Topogr. 2015, 28(5): 680-690.
Google Scholar
[33]
L Acqualagna, L Botrel, C Vidaurre, et al. Large- scale assessment of a fully automatic co-adaptive motor imagery-based brain computer interface. PLoS One. 2016, 11(2): e0148886.
Google Scholar
[34]
KJ Kokotilo, JJ Eng, A Curt. Reorganization and preservation of motor control of the brain in spinal cord injury: a systematic review. J Neurotrauma. 2009, 26(11): 2113-2126.
Google Scholar
[35]
G Pfurtscheller, P Linortner, R Winkler, et al. Discrimination of motor imagery-induced EEG patterns in patients with complete spinal cord injury. Comput Intell Neurosci. 2009: 104180.
Google Scholar
[36]
A Vuckovic, MA Hasan, B Osuagwu, et al. The influence of central neuropathic pain in paraplegic patients on performance of a motor imagery based Brain Computer Interface. Clin Neurophysiol. 2015, 126(11): 2170-2180.
Google Scholar
[37]
F Pichiorri, G Morone, M Petti, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015, 77(5): 851-865.
Google Scholar
[38]
ME Raichle, AM MacLeod, AZ Snyder, et al. A default mode of brain function. Proc Natl Acad Sci U S A. 2001, 98(2): 676-682.
Google Scholar
[39]
HI Suk, S Fazli, J Mehnert, et al. Predicting BCI subject performance using probabilistic spatio- temporal filters. PLoS One. 2014, 9(2): e87056.
Google Scholar
[40]
M Ahn, H Cho, S Ahn, et al. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS One. 2013, 8(11): e80886.
Google Scholar
[41]
M Ahn, S Ahn, JH Hong, et al. Gamma band activity associated with BCI performance: simultaneous MEG/EEG study. Front Hum Neurosci. 2013, 7: 848.
Google Scholar
[42]
M Grosse-Wentrup, B Schölkopf. High γ-power predicts performance in sensorimotor-rhythm brain- computer interfaces. J Neural Eng. 2012, 9(4): 046001.
Google Scholar
[43]
JL Reichert, SE Kober, C Neuper, et al. Resting-state sensorimotor rhythm (SMR) power predicts the ability to up-regulate SMR in an EEG-instrumental conditioning paradigm. Clin Neurophysiol. 2015, 126(11): 2068-2077.
Google Scholar
[44]
CJ Stam, EC van Straaten. The organization of physiological brain networks. Clin Neurophysiol. 2012, 123(6): 1067-1087.
Google Scholar
[45]
FL Li, TJ Liu, F Wang, et al. Relationships between the resting-state network and the P3: Evidence from a scalp EEG study. Sci Rep. 2015, 5: 15129.
Google Scholar
[46]
YJ Si, L Jiang, Q Tao, et al. Predicting individual decision-making responses based on the functional connectivity of resting-state EEG. J Neural Eng. 2019, 16(6): 066025.
Google Scholar
[47]
FL Li, JJ Wang, YY Liao, et al. Differentiation of schizophrenia by combining the spatial EEG brain network patterns of rest and task P300. IEEE Trans Neural Syst Rehabil Eng. 2019, 27(4): 594-602.
Google Scholar
[48]
Y Zhang, P Xu, D Guo, et al. Prediction of SSVEP-based BCI performance by the resting-state EEG network. J Neural Eng. 2013, 10(6): 066017.
Google Scholar
[49]
J Wu, R Srinivasan, A Kaur, et al. Resting-state cortical connectivity predicts motor skill acquisition. Neuroimage. 2014, 91: 84-90.
Google Scholar
[50]
R Zhang, DZ Yao, PA Valdés-Sosa, et al. Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng. 2015, 12(6): 066024.
Google Scholar
[51]
T Zhang, TJ Liu, FL Li, et al. Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network. Neuroimage. 2016, 134: 475-485.
Google Scholar
[52]
OA Mokienko, AV Chervyakov, SN Kulikova, et al. Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects. Front Comput Neurosci. 2013, 7: 168.
Google Scholar
[53]
T Mulder. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm. 2007, 114(10): 1265-1278.
Google Scholar
[54]
S Halder, D Agorastos, R Veit, et al. Neural mechanisms of brain-computer interface control. NeuroImage. 2011, 55(4): 1779-1790.
Google Scholar
[55]
XK Shu, SG Chen, L Yao, et al. Fast recognition of BCI-inefficient users using physiological features from EEG signals: a screening study of stroke patients. Front Neurosci. 2018, 12: 93.
Google Scholar
[56]
FL Li, CL Yi, LM Song, et al. Brain network reconfiguration during motor imagery revealed by a large-scale network analysis of scalp EEG. Brain Topogr. 2019, 32(2): 304-314.
Google Scholar
[57]
PA Valdés-Hernández, A Ojeda-González, E Martínez- Montes, et al. White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm. NeuroImage. 2010, 49(3): 2328-2339.
Google Scholar
[58]
TJ Whitford, CJ Rennie, SM Grieve, et al. Brain maturation in adolescence: concurrent changes in neuroanatomy and neurophysiology. Hum Brain Mapp. 2007, 28(3): 228-237.
Google Scholar
[59]
S Halder, B Varkuti, M Bogdan, et al. Prediction of brain-computer interface aptitude from individual brain structure. Front Hum Neurosci. 2013, 7: 105.
Google Scholar
[60]
JN Gong, C Luo, XB Chang, et al. White matter connectivity pattern associate with characteristics of scalp EEG signals. Brain Topogr. 2017, 30(6): 797-809.
Google Scholar
[61]
K Kasahara, CS DaSalla, M Honda, et al. Neuroanatomical correlates of brain-computer interface performance. Neuroimage. 2015, 110: 95-100.
Google Scholar
[62]
JR Wolpaw, N Birbaumer, WJ Heetderks, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehab Eng. 2000, 8(2): 164-173.
Google Scholar
[63]
T Hinterberger, R Veit, B Wilhelm, et al. Neuronal mechanisms underlying control of a brain-computer interface. Eur J Neurosci. 2005, 21(11): 3169-3181.
Google Scholar
[64]
F Nijboer, N Birbaumer, A Kübler. The influence of psychological state and motivation on brain- computer interface performance in patients with amyotrophic lateral sclerosis-a longitudinal study. Front Neurosci. 2010, 4: 55.
Google Scholar
[65]
M Witte, SE Kober, M Ninaus, et al. Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training. Front Hum Neurosci. 2013, 7: 478.
Google Scholar
[66]
EM Hammer, S Halder, B Blankertz, et al. Psychological predictors of SMR-BCI performance. Biol Psychol. 2012, 89(1): 80-86.
Google Scholar
[67]
C Jeunet, B N'Kaoua, S Subramanian, et al. Predicting mental imagery-based BCI performance from personality, cognitive profile and neurophysiological patterns. PLoS One. 2015, 10(12): e0143962.
Google Scholar
[68]
A Guillot, C Collet, VA Nguyen, et al. Brain activity during visual versus kinesthetic imagery: an fMRI study. Hum Brain Mapp. 2009, 30(7): 2157-2172.
Google Scholar
[69]
C Neuper, R Scherer, M Reiner, et al. Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Brain Res Cogn Brain Res. 2005, 25(3): 668-677.
Google Scholar
[70]
A Vuckovic, BA Osuagwu. Using a motor imagery questionnaire to estimate the performance of a Brain-Computer Interface based on object oriented motor imagery. Clin Neurophysiol. 2013, 124(8): 1586-1595.
Google Scholar
[71]
S Marchesotti, M Bassolino, A Serino, et al. Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep. 2016, 6: 24076.
Google Scholar
[72]
S Rimbert, N Gayraud, L Bougrain, et al. Can a subjective questionnaire be used as brain-computer interface performance predictor? Front Hum Neurosci. 2018, 12: 529.
Google Scholar
[73]
DJ Krusienski, M Grosse-Wentrup, F Galán, et al. Critical issues in state-of-the-art brain-computer interface signal processing. J Neural Eng. 2011, 8(2): 025002.
Google Scholar
[74]
V Mondini, AL Mangia, A Cappello. EEG-based BCI system using adaptive features extraction and classification procedures. Comput Intell Neurosci. 2016, 2016: 4562601.
Google Scholar
[75]
A Singh, S Lal, HW Guesgen. Reduce calibration time in motor imagery using spatially regularized symmetric positives-definite matrices based classification. Sensors. 2019, 19(2): E379.
Google Scholar
[76]
C Vidaurre, C Sannelli, KR Müller, et al. Co- adaptive calibration to improve BCI efficiency. J Neural Eng. 2011, 8(2): 025009.
Google Scholar
[77]
G Pfurtscheller, C Neuper. Motor imagery and direct brain-computer communication. Proc IEEE. 2001, 89(7): 1123-1134.
Google Scholar
[78]
YY Tang, YH Ma, JH Wang, et al. Short-term meditation training improves attention and self- regulation. Proc Natl Acad Sci USA. 2007, 104(43): 17152-17156.
Google Scholar
[79]
P Eskandari, A Erfanian. Improving the performance of brain-computer interface through meditation practicing. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver, BC, Canada, 2008, pp 662-665.
[80]
LF Tan, Z Dienes, A Jansari, et al. Effect of mindfulness meditation on brain-computer interface performance. Conscious Cogn. 2014, 23: 12-21.
Google Scholar
[81]
YQ Tan, LF Tan, SY Mok, et al. Effect of short term meditation on brain-computer interface performance. J Med Bioeng. 2015, 4(2): 135-138.
Google Scholar
[82]
M Gregg, C Hall, A Butler. The MIQ-RS: a suitable option for examining movement imagery ability. Evid Based Complement Alternat Med. 2010, 7(2): 249-257.
Google Scholar
[83]
C Dettmers, M Benz, J Liepert, et al. Motor imagery in stroke patients, or plegic patients with spinal cord or peripheral diseases. Acta Neurol Scand. 2012, 126(4): 238-247.
Google Scholar
[84]
BA Osuagwu, A Vuckovic. Similarities between explicit and implicit motor imagery in mental rotation of hands: an EEG study. Neuropsychologia. 2014, 65: 197-210.
Google Scholar
[85]
BA Osuagwu, M Zych, A Vuckovic. Is implicit motor imagery a reliable strategy for a brain- computer interface? IEEE Trans Neural Syst Rehabil Eng. 2017, 25(12): 2239-2248.
Google Scholar
[86]
XK Shu, L Yao, XJ Sheng, et al. Enhanced motor imagery-based BCI performance via tactile stimulation on unilateral hand. Front Hum Neurosci. 2017, 11: 585.
Google Scholar
[87]
C Zich, S Debener, C Kranczioch, et al. Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery. Neuroimage. 2015, 114: 438-447.
Google Scholar
[88]
JJ Meng, B He. Exploring training effect in 42 human subjects using a non-invasive sensorimotor rhythm based online BCI. Front Hum Neurosci. 2019, 13: 128.
Google Scholar
[89]
A Kaplan, A Vasilyev, S Liburkina, et al. Poor BCI performers still could benefit from motor imagery training. In Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience. Lecture Notes in Computer Science. DD Schmorrow, CM Fidopiastis, Eds. Cham, USA: Springer International Publishing, 2016.
[90]
C Jeunet, E Jahanpour, F Lotte. Why standard brain- computer interface (BCI) training protocols should be changed: an experimental study. J Neural Eng. 2016, 13(3): 036024.
Google Scholar
[91]
L Botrel, L Acqualagna, B Blankertz, et al. Short progressive muscle relaxation or motor coordination training does not increase performance in a brain- computer interface based on sensorimotor rhythms (SMR). Int J Psychophysiol. 2017, 121: 29-37.
Google Scholar
[92]
L Botrel, A Kübler. Week-long visuomotor coordination and relaxation trainings do not increase sensorimotor rhythms (SMR) based brain-computer interface performance. Behav Brain Res. 2019, 372: 111993.
Google Scholar
[93]
HF Chen, Q Yang, W Liao, et al. Evaluation of the effective connectivity of supplementary motor areas during motor imagery using Granger causality mapping. Neuroimage. 2009, 47(4): 1844-1853.
Google Scholar
[94]
Q Gao, XJ Duan, HF Chen. Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality. Neuroimage. 2011, 54(2): 1280-1288.
Google Scholar
[95]
FL Li, WJ Peng, YL Jiang, et al. The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG. Int J Neural Syst. 2019, 29(1): 1850016.
Google Scholar
[96]
C Brunner, R Scherer, B Graimann, et al. Online control of a brain-computer interface using phase synchronization. IEEE Trans Biomed Eng. 2006, 53(12 Pt 1): 2501-2506.
Google Scholar
[97]
M Billinger, C Brunner, GR Müller-Putz. Single-trial connectivity estimation for classification of motor imagery data. J Neural Eng. 2013, 10(4): 046006.
Google Scholar
[98]
R Zhang, XP Li, YW Wang, et al. Using brain network features to increase the classification accuracy of MI-BCI inefficiency subject. IEEE Access. 2019, 7: 74490-74499.
Google Scholar
[99]
YR Tabar, U Halici. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. 2017, 14(1): 016003.
Google Scholar
[100]
RT Schirrmeister, JT Springenberg, LDJ Fiederer, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017, 38(11): 5391-5420.
Google Scholar
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Publication history

Received: 28 June 2020
Revised: 22 July 2020
Accepted: 31 July 2020
Published: 04 February 2021
Issue date: September 2020

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© The authors 2020

Acknowledgements

This research was supported by grants from the National Natural Science Foundation of China (NSFC; Grant No. 61603344, No. 61961160705, No. #U19A2082) and the Key Research Projects of Henan Higher Education Institutions (Project No. 16A120008).

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