Journal Home > Volume 6 , Issue 3

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.


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Rui Zhang1Fali Li2Tao Zhang3Dezhong Yao1,2Peng 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
Received: 28 June 2020 Revised: 22 July 2020 Accepted: 31 July 2020 Published: 04 February 2021 Issue date: September 2020
References(100)
[1]
JJ Vidal. Toward direct brain-computer communication. Annu Rev Biophys Bioeng. 1973, 2(1): 157-180.
[2]
JR Wolpaw, N Birbaumer, DJ McFarland, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002, 113(6): 767-791.
[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.
[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.
[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.
[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.
[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.
[8]
SK Gao, YJ Wang, XR Gao, et al. Visual and auditory brain-computer interfaces. IEEE Trans Biomed Eng. 2014, 61(5): 1436-1447.
[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.
[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.
[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.
[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.
[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.
[14]
G Pfurtscheller, FH Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999, 110(11): 1842-1857.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[25]
C Vidaurre, B Blankertz. Towards a cure for BCI illiteracy. Brain Topogr. 2010, 23(2): 194-198.
[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.
[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.
[28]
MC Thompson. Critiquing the concept of BCI illiteracy. Sci Eng Ethics. 2019, 25(4): 1217-1233.
[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.
[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.
[31]
B Blankertz, C Sannelli, S Halder, et al. Neurophysiological predictor of SMR-based BCI performance. NeuroImage. 2010, 51(4): 1303-1309.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[39]
HI Suk, S Fazli, J Mehnert, et al. Predicting BCI subject performance using probabilistic spatio- temporal filters. PLoS One. 2014, 9(2): e87056.
[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.
[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.
[42]
M Grosse-Wentrup, B Schölkopf. High γ-power predicts performance in sensorimotor-rhythm brain- computer interfaces. J Neural Eng. 2012, 9(4): 046001.
[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.
[44]
CJ Stam, EC van Straaten. The organization of physiological brain networks. Clin Neurophysiol. 2012, 123(6): 1067-1087.
[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.
[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.
[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.
[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.
[49]
J Wu, R Srinivasan, A Kaur, et al. Resting-state cortical connectivity predicts motor skill acquisition. Neuroimage. 2014, 91: 84-90.
[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.
[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.
[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.
[53]
T Mulder. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm. 2007, 114(10): 1265-1278.
[54]
S Halder, D Agorastos, R Veit, et al. Neural mechanisms of brain-computer interface control. NeuroImage. 2011, 55(4): 1779-1790.
[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.
[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.
[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.
[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.
[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.
[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.
[61]
K Kasahara, CS DaSalla, M Honda, et al. Neuroanatomical correlates of brain-computer interface performance. Neuroimage. 2015, 110: 95-100.
[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.
[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.
[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.
[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.
[66]
EM Hammer, S Halder, B Blankertz, et al. Psychological predictors of SMR-BCI performance. Biol Psychol. 2012, 89(1): 80-86.
[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.
[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.
[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.
[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.
[71]
S Marchesotti, M Bassolino, A Serino, et al. Quantifying the role of motor imagery in brain-machine interfaces. Sci Rep. 2016, 6: 24076.
[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.
[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.
[74]
V Mondini, AL Mangia, A Cappello. EEG-based BCI system using adaptive features extraction and classification procedures. Comput Intell Neurosci. 2016, 2016: 4562601.
[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.
[76]
C Vidaurre, C Sannelli, KR Müller, et al. Co- adaptive calibration to improve BCI efficiency. J Neural Eng. 2011, 8(2): 025009.
[77]
G Pfurtscheller, C Neuper. Motor imagery and direct brain-computer communication. Proc IEEE. 2001, 89(7): 1123-1134.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[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.
[99]
YR Tabar, U Halici. A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng. 2017, 14(1): 016003.
[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.
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

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

Copyright

© 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).

Rights and permissions

This article is published with open access at journals.sagepub.com/home/BSA

Creative Commons Non Commercial CC BY- NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/ en-us/nam/open-access-at-sage).

Return