Machine-learning-based coadaptive calibration for brain-computer interfaces
Open accessAbstract
Brain-computer interfaces (BCIs) allow users to control a computer application by brain activity as acquired (e.g., by EEG). In our classic machine learning approach to BCIs, the participants undertake a calibration measurement without feedback to acquire data to train the BCI system. After the training, the user can control a BCI and improve the operation through some type of feedback. However, not all BCI users are able to perform sufficiently well during feedback operation. In fact, a nonnegligible portion of participants (estimated 15%–30%) cannot control the system (a BCI illiteracy problem, generic to all motor-imagery-based BCIs). We hypothesize that one main difficulty for a BCI user is the transition from offline calibration to online feedback. In this work, we investigate adaptive machine learning methods to eliminate offline calibration and analyze the performance of 11 volunteers in a BCI based on the modulation of sensorimotor rhythms. We present an adaptation scheme that individually guides the user. It starts with a subject-independent classifier that evolves to a subject-optimized state-of-the-art classifier within one session while the user interacts continuously. These initial runs use supervised techniques for robust coadaptive learning of user and machine. Subsequent runs use unsupervised adaptation to track the features’ drift during the session and provide an unbiased measure of BCI performance. Using this approach, without any offline calibration, six users, including one novice, obtained good performance after 3 to 6 minutes of adaptation. More important, this novel guided learning also allows participants with BCI illiteracy to gain significant control with the BCI in less than 60 minutes. In addition, one volunteer without sensorimotor idle rhythm peak at the beginning of the BCI experiment developed it during the course of the session and used voluntary modulation of its amplitude to control the feedback application.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Direct evidence of learned SMR modulation at the BCI feature level is rare or incomplete, derived in able-bodied populations and not longitudinal.
“Direct evidence of learned SMR modulation at the BCI feature level is, in fact, rare or incomplete, derived in able-bodied populations and not longitudinal [10,23,26,28,38–41].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [26]
Other studies have reported emergence of SMR modulations, but given the short number of experimental sessions, the observed neurophysiological patterns might only be indicative of transitory effects rather than consolidated subject learning.
“Other studies have reported emergence of such SMR modulations [26,28,38,39], but given the short number of experimental sessions they carried out, the observed neurophysiological patterns might only be indicative of transitory effects rather than consolidated subject learning.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [26]
Cites
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927 · see citing works
Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan, A. H., Branner, A., Chen, D., Penn, R. D., & Donoghue, J. P. (2006) · Nature · doi:10.1038/nature04970 · Cited by 3,295 · see citing works
A well-conditioned estimator for large-dimensional covariance matrices
Ledoit, O., & Wolf, M. (2004) · Journal of Multivariate Analysis · doi:10.1016/s0047-259x(03)00096-4 · Cited by 3,046 · see citing works
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 4× · see citing works
“…EEG features of large complexity that are fitted better to the individual characteristics of brain patterns of each user (see Blankertz et al., 2007; Blankertz, Tomioka, et al., 2008; Dornhege, Blankertz, Curio, & M¨uller, 2004; Dornhege et al., 2007; M¨uller et al., 2008; M¨uller, Anderson, &…”
BackgroundMethodInfluentialSelf-citationLearning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761 · see citing works
Schäfer, J., & Strimmer, K. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1175 · Cited by 1,556 · see citing works
Honey, I Shrunk the Sample Covariance Matrix
Ledoit, O., & Wolf, M. (2004) · The Journal of Portfolio Management · doi:10.3905/jpm.2004.110 · Cited by 1,279 · see citing works
A brain–computer interface using electrocorticographic signals in humans
Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G., & Moran, D. W. (2004) · Journal of Neural Engineering · doi:10.1088/1741-2560/1/2/001 · Cited by 1,188 · see citing works
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
Sugiyama, M. (2007) · Cited by 1,016 · see citing works
Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest
Pfurtscheller, G. (1992) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(92)90133-3 · Cited by 981 · see citing works
Blankertz, B., Dornhege, G., Krauledat, M., Müller, K., & Curio, G. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.01.051 · Cited by 941 · see citing works
Self-citationSpatial filter selection for EEG-based communication
McFarland, D. J., McCane, L. M., David, S. V., & Wolpaw, J. R. (1997) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00022-2 · Cited by 937 · see citing works
Toward Brain-Computer Interfacing
Dornhege, G., Millán, J. D. R., Hinterberger, T., McFarland, D. J., & Müller, K.-R. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.001.0001 · Cited by 789 · see citing works
Self-citationCovariate Shift Adaptation by Importance Weighted Cross Validation
Sugiyama, M., Krauledat, M., & Müller, K. (2007) · Cited by 710 · see citing works
Self-citationNeurophysiological predictor of SMR-based BCI performance
Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K.-R., Curio, G., & Dickhaus, T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.03.022 · Cited by 674 · see citing works
Self-citationDornhege, G., Blankertz, B., Curio, G., & Müller, K. R. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827088 · Cited by 632 · see citing works
Self-citationSchwartz, A. B. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144233 · Cited by 560 · see citing works
Brain-computer communication: Unlocking the locked in.
Kübler, A., Kotchoubey, B., Kaiser, J., Wolpaw, J. R., & Birbaumer, N. (2001) · Psychological Bulletin · doi:10.1037/0033-2909.127.3.358 · Cited by 554 · see citing works
Two-dimensional movement control using electrocorticographic signals in humans
Schalk, G., Miller, K. J., Anderson, N., Wilson, J. A., Smyth, M. D., Ojemann, J. G., Moran, D. W., Wolpaw, J. R., & Leuthardt, E. C. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/1/008 · Cited by 481 · see citing works
Müller, K., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., & Blankertz, B. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.09.022 · Cited by 475 · see citing works
Self-citationTowards adaptive classification for BCI
Shenoy, P., Krauledat, M., Blankertz, B., Rao, R. P. N., & Müller, K. (2006) · Journal of Neural Engineering · doi:10.1088/1741-2560/3/1/r02 · Cited by 436 · see citing works
Self-citationBrain–computer interface systems: progress and prospects
Allison, B. Z., Wolpaw, E. W., & Wolpaw, J. R. (2007) · Expert Review of Medical Devices · doi:10.1586/17434440.4.4.463 · Cited by 424 · see citing works
Linear and nonlinear methods for brain-computer interfaces
Müller, K. R., Anderson, C. W., & Birch, G. E. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814484 · Cited by 361 · see citing works
Self-citationThe Berlin brain-computer Interface: accurate performance from first-session in BCI-naive subjects
Blankertz, B., Losch, F., Krauledat, M., Dornhege, G., Curio, G., & Muller, K.-R. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.923152 · Cited by 338 · see citing works
Self-citationPrediction of arm movement trajectories from ECoG-recordings in humans
Tobias Pistohl, A., Tonio Ball, A., Schulze‐Bonhage, A., Aertsen, A., & Mehring, C. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.10.001 · Cited by 330 · see citing works
Nijboer (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00055 · Cited by 323 · see citing works
Time Domain Parameters as a feature for EEG-based Brain–Computer Interfaces
Vidaurre, C., Krämer, N., Blankertz, B., & Schlögl, A. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.07.020 · Cited by 304 · see citing works
Self-citationVolitional control of neural activity: implications for brain–computer interfaces
Fetz, E. E. (2007) · The Journal of Physiology · doi:10.1113/jphysiol.2006.127142 · Cited by 266 · see citing works
Finding Stationary Subspaces in Multivariate Time Series
von Bünau, P., Meinecke, F. C., Király, F. J., & Müller, K. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.214101 · Cited by 264 · see citing works
Self-citationSubject-independent mental state classification in single trials
Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K., & Grozea, C. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.06.003 · Cited by 253 · see citing works
Self-citationA review on directional information in neural signals for brain-machine interfaces
Waldert, S., Tobias Pistohl, A., Braun, C., Tonio Ball, A., Aertsen, A., & Mehring, C. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.08.007 · Cited by 207 · see citing works
Vidaurre, C., Schloogl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.873542 · Cited by 195 · see citing works
Self-citationTowards a robust BCI: error potentials and online learning
Buttfield, A., Ferrez, P. W., & Millán, J. D. R. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875555 · Cited by 181 · see citing works
Predictability of Brain-Computer Communication
Kübler, A., Neumann, N., Wilhelm, B., Hinterberger, T., & Birbaumer, N. (2004) · Journal of Psychophysiology · doi:10.1027/0269-8803.18.23.121 · Cited by 171 · see citing works
A regularized discriminative framework for EEG analysis with application to brain–computer interface
Tomioka, R., & Müller, K. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.07.045 · Cited by 167 · see citing works
Self-citationMillán, J. D. R., Frédéric Renkens, B., Josep Mouriño, C., & Gerstner, W. (2004) · Artificial Intelligence · doi:10.1016/j.artint.2004.05.008 · Cited by 142 · see citing works
Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces
Vidaurre, C., Schlögl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.888836 · Cited by 142 · see citing works
Self-citationUnsupervised Brain Computer Interface Based on Intersubject Information and Online Adaptation
Lu, S., Guan, C., & Zhang, H. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2015197 · Cited by 122 · see citing works
Quasi-movements: A novel motor–cognitive phenomenon
Nikulin, V. V., Hohlefeld, F. U., Jacobs, A. M., & Curio, G. (2007) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2007.10.008 · Cited by 115 · see citing works
Physiological regulation of thinking: brain–computer interface (BCI) research
Birbaumer, N., Weber, C., Neuper, C., Buch, E. R., Haapen, K., & Cohen, L. G. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)59024-7 · Cited by 112 · see citing works
Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces
Williamson, J. H., Murray‐Smith, R., Blankertz, B., Krauledat, M., & Müller, K. R. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.05.009 · Cited by 98 · see citing works
Self-citationLeuthardt, E. C., Freudenberg, Z., Bundy, D. T., & Roland, J. L. (2009) · Neurosurgical FOCUS · doi:10.3171/2009.4.focus0980 · Cited by 92 · see citing works
A fully implantable 96-channel neural data acquisition system
Rizk, M., Bossetti, C. A., Jochum, T. A., Callender, S. H., Nicolelis, M. A. L., Turner, D. A., & Wolf, P. D. (2009) · Journal of Neural Engineering · doi:10.1088/1741-2560/6/2/026002 · Cited by 89 · see citing works
Human Brain—Computer Interface
Pfurtscheller, G., Neuper, C., & Birbaumer, N. (2004) · Frontiers in neuroscience · doi:10.1201/9780203503584.ch14 · Cited by 81 · see citing works
Adaptive Classification for Brain Computer Interfaces
Blumberg, J., Rickert, J., Waldert, S., Schulze‐Bonhage, A., Aertsen, A., & Mehring, C. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352845 · Cited by 77 · see citing works
Motor Cortex in Voluntary Movements
(2004) · Frontiers in neuroscience · doi:10.1201/9780203503584.supl · Cited by 67 · see citing works
Implementation of a Brain-Computer Interface Based on Three States of Motor Imagery
Wang, Y., Hong, B., Gao, X., & Gao, S. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353477 · Cited by 61 · see citing works
Proceedings of the 4th International Brain-Computer Interface Workshop and Training Course 2008
Muller-Putz, G. R., Brunner, C., Leeb, R., Scherer, R., Schlögl, A., Wriessnegger, S. C., & Pfurtscheller, G. (2006) · Cited by 38 · see citing works
Analysis of Nonstationarities in EEG Signals for Improving Brain-Computer Interface Performance
Krauledat, J. M. (2008) · DepositOnce · doi:10.14279/depositonce-1817 · Cited by 36 · see citing works
A well conditioned estimator for large dimensional covariance matricesPreprint
Wolf, M., & Ledoit, O. (2000) · RePEc: Research Papers in Economics · Cited by 36 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239 · see citing works
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143 · see citing works
Applications of Deep Learning and Reinforcement Learning to Biological Data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 852 · see citing works
Introduction to machine learning for brain imaging
Lemm, S., Blankertz, B., Dickhaus, T., & Müller, K. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.004 · Cited by 626 · see citing works
Self-citationEnhanced performance by a hybrid NIRS–EEG brain computer interface
Fazli, S., Mehnert, J., Steinbrink, J. M., Curio, G., Villringer, A., Müller, K., & Blankertz, B. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.084 · Cited by 607 · see citing works
Self-citationMyoelectric Control of Artificial Limbs—Is There a Need to Change Focus? [In the Spotlight]
Jiang, N., Dosen, S. S., Müller, K.-R., & Farina, D. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2203480 · Cited by 497 · see citing works
Self-citationDeep Learning in Mining Biological Data
Mahmud, M., Kaiser, M. S., McGinnity, T. M., & Hussain, A. (2021) · Cognitive Computation · doi:10.1007/s12559-020-09773-x · Cited by 465 · see citing works
Visual and Auditory Brain–Computer Interfaces
Gao, S., Wang, Y., Gao, X., & Hong, B. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2300164 · Cited by 451 · see citing works
Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control
Hahne, J. M., BieBmann, F., Jiang, N., Rehbaum, H., Farina, D., Meinecke, F. C., Müller, K. R., & Parra, L. C. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2305520 · Cited by 369 · see citing works
Self-citationThe Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Blankertz, B., Tangermann, M., Vidaurre, C., Fazli, S., Sannelli, C., Haufe, S., Maeder, C., Ramsey, L., Sturm, I., Curio, G., et al. (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00198 · Cited by 357 · see citing works
Self-citationLotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327 · see citing works
Performance variation in motor imagery brain–computer interface: A brief review
Ahn, M., & Jun, S. C. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.01.033 · Cited by 319 · see citing works
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307 · see citing works
Open Access Dataset for EEG+NIRS Single-Trial Classification
Shin, J., von Lühmann, A., Blankertz, B., Kim, D.-W., Jeong, J., Hwang, H., & Müller, K. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2628057 · Cited by 283 · see citing works
Self-citationPsychological predictors of SMR-BCI performance
Hammer, E. M., Halder, S., Blankertz, B., Sannelli, C., Dickhaus, T., Kleih, S., Müller, K.-R., & Kübler, A. (2012) · Biological Psychology · doi:10.1016/j.biopsycho.2011.09.006 · Cited by 267 · see citing works
Self-citationIdentification of Risk Factors Associated with Obesity and Overweight—A Machine Learning Overview
Chatterjee, A., Gerdes, M. W., & Martínez, S. (2020) · Sensors · doi:10.3390/s20092734 · Cited by 245 · see citing works
Towards reconstructing intelligible speech from the human auditory cortex
Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-37359-z · Cited by 221 · see citing works
Hramov, A. E., Maksimenko, V., & Pisarchik, A. N. (2021) · Physics Reports · doi:10.1016/j.physrep.2021.03.002 · Cited by 218 · see citing works
Transferring Subspaces Between Subjects in Brain--Computer Interfacing
Samek, W., Meinecke, F. C., & Müller, K. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2253608 · Cited by 216 · see citing works
Self-citationAlkoby, O., Abu-Rmileh, A., Shriki, O., & Todder, D. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.12.050 · Cited by 212 · see citing works
Stationary common spatial patterns for brain–computer interfacing
Samek, W., Vidaurre, C., Müller, K., & Kawanabe, M. (2012) · Journal of Neural Engineering · doi:10.1088/1741-2560/9/2/026013 · Cited by 211 · see citing works
Self-citationSingh, A. S. G., Hussain, A. A., Lal, S. P., & Guesgen, H. W. (2021) · Sensors · doi:10.3390/s21062173 · Cited by 195 · see citing works
Brain-Computer Interfacing: An Introduction
Rao, R. P. N. (2019) · Cited by 181 · see citing works
Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI
Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2189584 · Cited by 180 · see citing works
Self-citationDivergence-Based Framework for Common Spatial Patterns Algorithms
Samek, W., Kawanabe, M., & Müller, K. (2013) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2013.2290621 · Cited by 179 · see citing works
Self-citationSpüler, M., Rosenstiel, W., & Bogdan, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051077 · Cited by 175 · see citing works
Co-adaptive calibration to improve BCI efficiency
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025009 · Cited by 169 · see citing works
Self-citationThe Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
Perdikis, S., Tonin, L., Saeedi, S., Schneider, C., & Millán, J. D. R. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2003787 · Cited by 166 · see citing works
Distributed cortical adaptation during learning of a brain–computer interface task
Wander, J. D., Blakely, T., Miller, K. J., Weaver, K. E., Johnson, L. A., Olson, J. D., Fetz, E. E., Rao, R. P. N., & Ojemann, J. G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221127110 · Cited by 157 · see citing works
A Survey on Unmanned Aerial Vehicle Remote Control Using Brain–Computer Interface
Nourmohammadi, A., Jafari, M., & Zander, T. O. (2018) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2018.2830647 · Cited by 157 · see citing works
A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control
Allison, B. Z., Brunner, C., Altstätter, C., Wagner, I. C., Grissmann, S., & Neuper, C. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.06.022 · Cited by 143 · see citing works
Hybrid deep neural network using transfer learning for EEG motor imagery decoding
Zhang, R., Zong, Q., Dou, L., Zhao, X., Tang, Y., & Li, Z. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102144 · Cited by 139 · see citing works
Zhang, X., Ma, Z., Zheng, H., Li, T., Chen, K., Wang, X., Liu, C., Xu, L., Wu, X., Lin, D., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.11.109 · Cited by 137 · see citing works
Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. H. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2239310 · Cited by 133 · see citing works
Toward a Direct Measure of Video Quality Perception Using EEG
Scholler, S., Bosse, S., Treder, M. S., Blankertz, B., Curio, G., Müller, K., & Wiegand, T. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2187672 · Cited by 130 · see citing works
Self-citationLee, M.-H., Fazli, S., Mehnert, J., & Lee, S. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.010 · Cited by 118 · see citing works
An analysis of performance evaluation for motor-imagery based BCI
Thomas, E. M., Dyson, M., & Clerc, M. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/3/031001 · Cited by 116 · see citing works
A review of user training methods in brain computer interfaces based on mental tasks
Roc, A., Pillette, L., Mladenović, J., Benaroch, C., N’Kaoua, B., Jeunet, C., & Lotte, F. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abca17 · Cited by 114 · see citing works
Holz, E. M., Höhne, J., Staiger-Sälzer, P., Tangermann, M., & Kübler, A. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.08.001 · Cited by 111 · see citing works
Predicting BCI Subject Performance Using Probabilistic Spatio-Temporal Filters
Suk, H., Fazli, S., Mehnert, J., Müller, K., & Lee, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087056 · Cited by 105 · see citing works
Self-citationHahne, J. M., Dähne, S., Hwang, H., Müller, K., & Parra, L. C. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2401134 · Cited by 105 · see citing works
Self-citationBrain-computer interfacing using modulations of alpha activity induced by covert shifts of attention
Treder, M. S., Bahramisharif, A., Schmidt, N. M., van Gerven, M. A. J., & Blankertz, B. (2011) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-8-24 · Cited by 104 · see citing works
Self-citationSannelli, C., Vidaurre, C., Müller, K., & Blankertz, B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0207351 · Cited by 103 · see citing works
Self-citationFirst Steps Toward a Motor Imagery Based Stroke BCI: New Strategy to Set up a Classifier
Kaiser, V., Kreilinger, A., Muller-Putz, G. R., & Neuper, C. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00086 · Cited by 102 · see citing works
EEG Data Space Adaptation to Reduce Intersession Nonstationarity in Brain-Computer Interface
Arvaneh, M., Guan, C., Ang, K. K., & Quek, C. H. (2013) · Neural Computation · doi:10.1162/neco_a_00474 · Cited by 88 · see citing works
Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications
Douibi, K., Bars, S. L., Lemontey, A., Nag, L., Balp, R., & Bréda, G. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.705064 · Cited by 86 · see citing works
Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges
Ienca, M., & Ignatiadis, K. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740352 · Cited by 84 · see citing works
From classic motor imagery to complex movement intention decoding
Muller-Putz, G. R., Schwarz, A., Pereira, J., & Ofner, P. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.04.017 · Cited by 81 · see citing works
Pohlmeyer, E. A., Mahmoudi, B., Geng, S., Prins, N. W., & Sanchez, J. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087253 · Cited by 76 · see citing works
Immediate brain plasticity after one hour of brain–computer interface (BCI)
Nierhaus, T., Vidaurre, C., Sannelli, C., Müller, K., & Villringer, A. (2019) · The Journal of Physiology · doi:10.1113/jp278118 · Cited by 76 · see citing works
Self-citationDefining and quantifying users’ mental imagery-based BCI skills: a first step
Lotte, F., & Jeunet, C. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aac577 · Cited by 74 · see citing works
Shin, J., Müller, K.-R., & Hwang, H.-J. (2016) · Scientific Reports · doi:10.1038/srep36203 · Cited by 73 · see citing works
Self-citationInterface Prostheses With Classifier-Feedback-Based User Training
Fang, Y., Zhou, D., Li, K., & Liu, H. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2641584 · Cited by 72 · see citing works
Silva, G. A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00843 · Cited by 71 · see citing works
Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review
Jeunet, C., Lotte, F., Batail, J.-M. V., Philip, P., & Micoulaud‐Franchi, J. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.03.013 · Cited by 67 · see citing works
A mathematical model for the two-learners problem
Müller, J. S., Vidaurre, C., Schreuder, M., Meinecke, F. C., von Bünau, P., & Müller, K. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa620b · Cited by 66 · see citing works
Self-citationEEG-Based Brain-Computer Interfaces
Wang, Y., Nakanishi, M., & Zhang, D. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-13-2050-7_2 · Cited by 61 · see citing works
Large-Scale Assessment of a Fully Automatic Co-Adaptive Motor Imagery-Based Brain Computer Interface
Acqualagna, L., Botrel, L., Vidaurre, C., Kübler, A., & Blankertz, B. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148886 · Cited by 58 · see citing works
Self-citationBanville, H. J., & Falk, T. H. (2016) · Brain-Computer Interfaces · doi:10.1080/2326263x.2015.1134958 · Cited by 58 · see citing works
Brain–computer interfaces: a powerful tool for scientific inquiry
Wander, J. D., & Rao, R. P. N. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.013 · Cited by 56 · see citing works
Improving brain–computer interface classification using adaptive common spatial patterns
Song, X., & Yoon, S.-C. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.03.023 · Cited by 56 · see citing works
Ensembles of adaptive spatial filters increase BCI performance: an online evaluation
Sannelli, C., Vidaurre, C., Müller, K., & Blankertz, B. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046003 · Cited by 55 · see citing works
Self-citationPre-Stimulus Sensorimotor Rhythms Influence Brain–Computer Interface Classification Performance
Maeder, C. L., Sannelli, C., Haufe, S., & Blankertz, B. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2205707 · Cited by 54 · see citing works
Self-citationOnline Adaptation Boosts SSVEP-Based BCI Performance
Wong, C. M., Wang, Z., Nakanishi, M., Wang, B., Rosa, A., Chen, C. L. P., Jung, T., & Wan, F. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3133594 · Cited by 51 · see citing works
Facilitating motor imagery-based brain–computer interface for stroke patients using passive movement
Arvaneh, M., Guan, C., Ang, K. K., Ward, T. E., Chua, K. S. G., Kuah, C. W. K., Joseph, G. J. E., Phua, K. S., & Wang, C. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2234-7 · Cited by 50 · see citing works
Context-aware adaptive spelling in motor imagery BCI
Perdikis, S., Leeb, R., & Millán, J. D. R. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/3/036018 · Cited by 49 · see citing works
Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review
Salahuddin, U., & Gao, P. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.728178 · Cited by 49 · see citing works
Comparison of EEG measurement of upper limb movement in motor imagery training system
Suwannarat, A., Pan-ngum, S., & Israsena, P. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0534-0 · Cited by 49 · see citing works
Selective Sensation Based Brain-Computer Interface via Mechanical Vibrotactile Stimulation
Yao, L., Meng, J., Zhang, D., Sheng, X., & Zhu, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064784 · Cited by 48 · see citing works
Brain-Computer Interface for Persons with Motor Disabilities - A Review
Anitha, T., Shanthi, N., Sathiyasheelan, R., Emayavaramban, G., & Rajendran, T. (2019) · The Open Biomedical Engineering Journal · doi:10.2174/1874120701913010127 · Cited by 47 · see citing works
CSP patches: an ensemble of optimized spatial filters. An evaluation study
Sannelli, C., Vidaurre, C., Müller, K., & Blankertz, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025012 · Cited by 44 · see citing works
Self-citationLi, Y., Bahn, S., Nam, C. S., & Lee, J. (2013) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2013.839903 · Cited by 44 · see citing works
Towards reconstructing intelligible speech from the human auditory cortexPreprint
Akbari, H., Khalighinejad, B., Herrero, J. L., Mehta, A. D., & Mesgarani, N. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/350124 · Cited by 44 · see citing works
Eliseyev, A., Auboiroux, V., Costecalde, T., Langar, L., Charvet, G., Mestais, C., Aksenova, T., & Benabid, A.-L. (2017) · Scientific Reports · doi:10.1038/s41598-017-16579-9 · Cited by 42 · see citing works
Artusi, X., Niazi, I. K., Lucas, M.-F., & Farina, D. (2011) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2011.2177920 · Cited by 41 · see citing works
Robust Common Spatial Filters with a Maxmin Approach
Kawanabe, M., Samek, W., Müller, K., & Vidaurre, C. (2013) · Neural Computation · doi:10.1162/neco_a_00544 · Cited by 37 · see citing works
Self-citationFunctional Neuroimaging in the New Era of Big Data
Li, X., Guo, N., & Li, Q. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.11.005 · Cited by 37 · see citing works
Decoding finger movement in humans using synergy of EEG cortical current signals
Yoshimura, N., Tsuda, H., Kawase, T., Kambara, H., & Koike, Y. (2017) · Scientific Reports · doi:10.1038/s41598-017-09770-5 · Cited by 37 · see citing works
Using fNIRS to Measure Mental Workload in the Real World
Peck, E. M., Afergan, D., Yuksel, B. F., Lalooses, F., & Jacob, R. J. K. (2014) · Human-computer interaction series · doi:10.1007/978-1-4471-6392-3_6 · Cited by 36 · see citing works
Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance
Vidaurre, C., Haufe, S., Jorajuría, T., Müller, K., & Nikulin, V. V. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.575081 · Cited by 35 · see citing works
Self-citationAdaptive Multiclass Classification for Brain Computer Interfaces
Llera, A., Gómez, V., & Kappen, H. J. (2014) · Neural Computation · doi:10.1162/neco_a_00592 · Cited by 34 · see citing works
Model and experiments to optimize co-adaptation in a simplified myoelectric control system
Couraud, M., Cattaert, D., Paclet, F., Oudeyer, P., & de Rugy, A. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa87cf · Cited by 33 · see citing works
Co-adaptive Training Improves Efficacy of a Multi-Day EEG-Based Motor Imagery BCI Training
Abu-Rmileh, A., Zakkay, E., Shmuelof, L., & Shriki, O. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00362 · Cited by 33 · see citing works
Lampe, T., Fiederer, L. D. J., Voelker, M., Knorr, A. G., Riedmiller, M., & Tonio Ball, A. (2014) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2557500.2557533 · Cited by 33 · see citing works
Adaptive Classification on Brain-Computer Interfaces Using Reinforcement Signals
Llera, A., Gómez, V., & Kappen, H. J. (2012) · Neural Computation · doi:10.1162/neco_a_00348 · Cited by 33 · see citing works
Adaptive Assistance for Brain-Computer Interfaces by Online Prediction of Command Reliability
Saeedi, S., Chavarriaga, R., Leeb, R., & Millán, J. D. R. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2501550 · Cited by 32 · see citing works
Shin, J., Kim, D.-W., Müller, K., & Hwang, H.-J. (2018) · Sensors · doi:10.3390/s18061827 · Cited by 32 · see citing works
Self-citationEffects of Task Complexity on Motor Imagery-Based Brain–Computer Interface
Mashat, M. E. M., Lin, C., & Zhang, D. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2936987 · Cited by 32 · see citing works
Unsupervised adaptation of brain-machine interface decoders
Gürel, T. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00164 · Cited by 32 · see citing works
Nguyen, C. H., Karavas, G. K., & Artemiadis, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212620 · Cited by 30 · see citing works
Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies
Yeung, D., Guerra, I. M., Barner-Rasmussen, I., Siponen, E., Farina, D., & Vujaklija, I. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3150665 · Cited by 30 · see citing works
Zhong, Y., Yao, L., Wang, J., & Wang, Y. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3201241 · Cited by 30 · see citing works
New Perspectives on Neuroengineering and Neurotechnologies: NSF-DFG Workshop Report
Moritz, C. T., Ruther, P., Goering, S., Stett, A., Ball, T., Burgard, W., Chudler, E. H., & Rao, R. P. N. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2543662 · Cited by 29 · see citing works
Improving the performance of P300 BCI system using different methods
Fouad, I. A., Labib, F. E.-Z. M., Mabrouk, M. S., Sharawy, A. A., & Sayed, A. Y. (2020) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-020-00268-1 · Cited by 29 · see citing works
Motor imagery based brain–computer interfaces
Scherer, R., & Vidaurre, C. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812892-3.00008-x · Cited by 29 · see citing works
Self-citationA robust and reliable online P300-based BCI system using Emotiv EPOC + headset
Fouad, I. A. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2020.1853840 · Cited by 28 · see citing works
Kim, D.-H., Shin, D., & Kam, T. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1194751 · Cited by 28 · see citing works
Neuromuscular electrical stimulation induced brain patterns to decode motor imagery
Vidaurre, C., Pascual, J., Ramos‐Murguialday, A., Lorenz, R., Blankertz, B., Birbaumer, N., & Müller, K. R. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.03.009 · Cited by 28 · see citing works
Self-citationWu, S., Bhadra, K., Giraud, A., & Marchesotti, S. (2024) · Brain Sciences · doi:10.3390/brainsci14030196 · Cited by 26 · see citing works
Brain–computer interfacing under distraction: an evaluation study
Brandl, S., Frølich, L., Höhne, J., Müller, K., & Samek, W. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/5/056012 · Cited by 26 · see citing works
Self-citation
- Authors
- Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B.
- Affiliations
- Technische Universität Berlin (DE)
- Corresponding author
- Vidaurre, Carmen
- Sannelli, Claudia
- Müller, Klaus-Robert
- Blankertz, Benjamin
- Venue
- Neural Computation
- Publisher
- MIT Press - Journals
- Published
- 2011-03
- Volume
- 23
- Issue
- 3
- Pages
- 791-816
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2068654413
- Cited by
- 207
- References
- 50
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 9 · 2022: 5 · 2021: 10 · 2020: 13 · 2019: 14 · 2018: 19 · 2017: 13 · 2016: 18 · 2015: 13 · 2014: 13 · 2013: 25 · 2012: 22
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- brain-computer interface, BCI illiteracy, motor imagery, sensorimotor rhythm, online adaptation, unsupervised adaptation, feature drift, EEG, novice users
- MeSH terms
- Adaptation, Physiological, Adaptation, Psychological, Algorithms, Artificial Intelligence, Brain, Calibration, Electroencephalography, Humans, Neuronal Plasticity, Feedback, Psychological, Brain-Computer Interfaces
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
