Heading for new shores! Overcoming pitfalls in BCI design
Open accessAbstract
Research in brain-computer interfaces has achieved impressive progress towards implementing assistive technologies for restoration or substitution of lost motor capabilities, as well as supporting technologies for able-bodied subjects. Notwithstanding this progress, effective translation of these interfaces from proof-of concept prototypes into reliable applications remains elusive. As a matter of fact, most of the current BCI systems cannot be used independently for long periods of time by their intended end-users. Multiple factors that impair achieving this goal have already been identified. However, it is not clear how do they affect the overall BCI performance or how they should be tackled. This is worsened by the publication bias where only positive results are disseminated, preventing the research community from learning from its errors. This paper is the result of a workshop held at the 6th International BCI meeting in Asilomar. We summarize here the discussion on concrete research avenues and guidelines that may help overcoming common pitfalls and make BCIs become a useful alternative communication device.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The authors' results are in line with the emerging belief about the need for more stimulating BCI training contexts.
“Our results are in line with the emerging belief about the need for more stimulating BCI training contexts [47,53,66].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [47]
The impact of training with the BCI application might be related to a need for getting accustomed to the actual application demands but probably also to increased user motivation provided by the gaming application and the goal of participating in an international competition.
“This might be related to a need for getting accustomed to the actual application demands [47] but probably also to increased user motivation [68] provided both by the gaming application and the goal of participating in an international competition [8].”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [47]
Various psychological factors, such as motivation, attention, and stress, have been implicated in these negative effects, which are quite frequent in MI BCI operation.
“Various psychological factors (such as motivation, attention, and stress) have been implicated in these negative effects [2,47,53,66–68], which unfortunately are quite frequent in MI BCI operation.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [47]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003 · see citing works
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496 · see citing works
The ASA Statement on <i>p</i> -Values: Context, Process, and Purpose
Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306 · see citing works
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631 · see citing works
Evoked-potential correlates of stimulus uncertainty
Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965) · Science · doi:10.1126/science.150.3700.1187 · Cited by 3,061 · see citing works
A review of classification algorithms for EEG-based brain–computer interfaces
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., & Arnaldi, B. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r01 · Cited by 2,665 · see citing works
Self-citationOptimal spatial filtering of single trial EEG during imagined hand movement
Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457 · see citing works
Brain-computer interface technology: a review of the first international meeting
Wolpaw, J. R., Birbaumer, N., Heetderks, W., McFarland, D. J., Peckham, P. H., Schalk, G., Donchin, E., Quatrano, L. A., Robinson, C. J., & Vaughan, T. M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/tre.2000.847807 · Cited by 2,322 · 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 3× · see citing works
“For instance, ERP and MRCP-based BCI typically reach better performances when using time-domain features, whereas SSVEP and mental imagery-based BCIs should use PSD or similar features [85, 86, 87].”
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577 · 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
Duncan, C. C., Barry, R. J., Connolly, J. F., Fischer, C., Michie, P. T., Näätänen, R., Polich, J., Reinvang, I., & Van Petten, C. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.07.045 · Cited by 1,191 · see citing works
Brain–Computer InterfacesPrinciples and Practice
Wolpaw, J. R., & Wolpaw, E. W. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195388855.001.0001 · Cited by 1,114 · see citing works
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & García-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 973 · 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
Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r03 · Cited by 907 · 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
Neuper, C., Scherer, R., Reiner, M., & Pfurtscheller, G. (2005) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2005.08.014 · Cited by 716 · 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
Pfurtscheller, G. (2010) · Frontiers in Neuroscience · doi:10.3389/fnpro.2010.00003 · Cited by 673 · see citing works
How many people are able to control a P300-based brain-computer interface (BCI)?
Guger, C., Daban, S., Sellers, E., Holzner, C., Krausz, G., Carabalona, R., Gramatica, F., & Edlinger, G. (2009) · Neuroscience Letters · doi:10.1016/j.neulet.2009.06.045 · Cited by 652 · see citing works
EMG and EOG artifacts in brain computer interface systems: A survey
Fatourechi, M., Bashashati, A., Ward, R. K., & Birch, G. E. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.10.019 · Cited by 634 · 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
A fully automated correction method of EOG artifacts in EEG recordings
Schlögl, A., Keinrath, C., Zimmermann, D., Scherer, R., Leeb, R., & Pfurtscheller, G. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.09.003 · Cited by 575 · see citing works
Self-citationDevelopment of ICF Core Sets for patients with chronic conditions
Ewert, T., Üstün, T. B., Chatterji, S., Kostanjsek, N. F. I., Stucki, G., & Cieza, A. (2004) · Journal of Rehabilitation Medicine · doi:10.1080/16501960410015353 · Cited by 524 · see citing works
Trends and issues in instructional design and technology
Anglin, G. J., & Ley, K. (2002) · Educational Technology Research and Development · doi:10.1007/bf02504986 · Cited by 475 · see citing works
A brain-computer interface for long-term independent home use
Sellers, E. W., Vaughan, T. M., & Wolpaw, J. R. (2010) · Amyotrophic Lateral Sclerosis · doi:10.3109/17482961003777470 · Cited by 400 · see citing works
Clerc, M., Bougrain, L., & Lotte, F. (2010) · Human-computer interaction series · doi:10.1007/978-1-84996-272-8 · Cited by 391 · see citing works
Self-citationBCI demographics: how many (and what kinds of) people can use an SSVEP BCI?
Allison, B., Luth, T., Valbuena, D., Teymourian, A., Volosyak, I., & Graser, A. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039495 · Cited by 344 · see citing works
Lotte, F. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2404941 · Cited by 327 · see citing works
Self-citationNijboer (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00055 · Cited by 323 · 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
Self-citationA Comprehensive Survey of Brain Interface Technology Designs
Mason, S. G., Bashashati, A., Fatourechi, M., Navarro, K. F., & Birch, G. E. (2006) · Annals of Biomedical Engineering · doi:10.1007/s10439-006-9170-0 · Cited by 299 · see citing works
How scientists fool themselves – and how they can stop
Nuzzo, R. (2015) · Nature · doi:10.1038/526182a · Cited by 296 · see citing works
Toward Unsupervised Adaptation of LDA for Brain–Computer Interfaces
Vidaurre, C., Kawanabe, M., von Bünau, P., Blankertz, B., & Müller, K. R. (2010) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2093133 · Cited by 296 · see citing works
Psychological 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-citationMatching Person & Technology (MPT) assessment process
Scherer, M. J., & Craddock, G. (2002) · Technology and Disability · doi:10.3233/tad-2002-14308 · Cited by 266 · see citing works
Motivation modulates the P300 amplitude during brain–computer interface use
Kleih, S. C., Nijboer, F., Halder, S., & Kübler, A. (2010) · Clinical Neurophysiology · doi:10.1016/j.clinph.2010.01.034 · Cited by 259 · see citing works
Self-citationA Self-Paced and Calibration-Less SSVEP-Based Brain–Computer Interface Speller
Cecotti, H. (2010) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2039594 · Cited by 253 · see citing works
Leeb, R., Sagha, H., Chavarriaga, R., & Millán, J. D. R. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025011 · Cited by 245 · see citing works
Self-citationKübler, A., Holz, E. M., Riccio, A., Zickler, C., Kaufmann, T., Kleih, S. C., Staiger-Sälzer, P., Desideri, L., Hoogerwerf, E.-J., & Donatella Mattia, A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112392 · Cited by 222 · see citing works
Self-citationEEG beta suppression and low gamma modulation are different elements of human upright walking
Seeber, M., Scherer, R., Wagner, J., Solis‐Escalante, T., & Muller-Putz, G. R. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00485 · Cited by 221 · see citing works
Self-citationTransferring 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
Huggins, J. E., Wren, P. A., & Gruis, K. L. (2011) · Amyotrophic Lateral Sclerosis · doi:10.3109/17482968.2011.572978 · Cited by 215 · see citing works
A framework for modelling the selection of assistive technology devices (ATDs)
Scherer, M. J., Jutai, J. W., Führer, M. J., Demers, L., & DeRuyter, F. (2007) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483100600845414 · Cited by 202 · see citing works
Jeunet, C., Jahanpour, E., & Lotte, F. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/3/036024 · Cited by 192 · see citing works
Self-citationLotte, F. (2014) · Springer eBooks · doi:10.1007/978-1-4471-6584-2_7 · Cited by 190 · see citing works
Self-citationClosing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery
Gomez-Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A., & Grosse‐Wentrup, M. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/3/036005 · Cited by 183 · see citing works
Seeber, M., Scherer, R., Wagner, J., Solis‐Escalante, T., & Müller-Putz, G. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.045 · Cited by 183 · see citing works
Self-citationJeunet, C., N’Kaoua, B., Subramanian, S., Hachet, M., & Lotte, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143962 · Cited by 182 · see citing works
Self-citationAutocalibration 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
FORCe: Fully online and automated artifact removal for brain-computer interfacing
Daly, I., Scherer, R., Billinger, M., & Muller-Putz, G. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2346621 · Cited by 174 · see citing works
Self-citationHolz, E. M., Botrel, L., Kaufmann, T., & Kübler, A. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2014.03.035 · Cited by 171 · see citing works
Borton, D., Micera, S., Millán, J. D. R., & Courtine, G. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3005968 · Cited by 171 · see citing works
Evolving Signal Processing for Brain–Computer Interfaces
Makeig, S., Kothe, C. A., Mullen, T. R., Bigdely-Shamlo, N., Zhang, Z., & Kreutz-Delgado, K. (2012) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2185009 · Cited by 163 · see citing works
Attention and P300-based BCI performance in people with amyotrophic lateral sclerosis
Riccio, A., Simione, L., Schettini, F., Pizzimenti, A., Inghilleri, M., Belardinelli, M. O., Mattia, D., & Cincotti, F. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00732 · Cited by 162 · see citing works
Muller-Putz, G. R., Leeb, R., Tangermann, M., Höhne, J., Kübler, A., Febo Cincotti, A., Donatella Mattia, A., Rupp, R., Müller, K., & Millán, J. D. R. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2411333 · Cited by 158 · 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
Advances in user-training for mental-imagery-based BCI control
Jeunet, C., N’Kaoua, B., & Lotte, F. (2016) · Progress in brain research · doi:10.1016/bs.pbr.2016.04.002 · Cited by 145 · see citing works
Self-citationCombining BCI with Virtual Reality: Towards New Applications and Improved BCI
Lotte, F., Faller, J., Guger, C., Renard, Y., Pfurtscheller, G., Lécuyer, A., & Leeb, R. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_10 · Cited by 144 · see citing works
Self-citationBrouwer, A.-M., Zander, T. O., van Erp, J. B. F., Korteling, J. E., & Bronkhorst, A. W. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00136 · Cited by 143 · see citing works
Evaluation Criteria for BCI Research
Schlögl, A., Kronegg, J., Huggins, J. E., & Mason, S. G. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.003.0024 · Cited by 141 · see citing works
Noninvasive brain-computer interface enables communication after brainstem stroke
Sellers, E. W., Ryan, D. B., & Hauser, C. K. (2014) · Science Translational Medicine · doi:10.1126/scitranslmed.3007801 · Cited by 140 · see citing works
Kaufmann, T., Holz, E. M., & Kübler, A. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00129 · Cited by 137 · see citing works
Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis
McCane, L. M., Sellers, E. W., McFarland, D. J., Mak, J. N., Carmack, C. S., Zeitlin, D., Wolpaw, J. R., & Vaughan, T. M. (2014) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.3109/21678421.2013.865750 · Cited by 129 · see citing works
Translating the Brain-Machine Interface
Thakor, N. V. (2013) · Science Translational Medicine · doi:10.1126/scitranslmed.3007303 · Cited by 128 · see citing works
Zickler, C., Halder, S., Kleih, S. C., Herbert, C., & Kübler, A. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.08.003 · Cited by 126 · see citing works
Self-citationThe effect of distinct mental strategies on classification performance for brain–computer interfaces
Friedrich, E. V. C., Scherer, R., & Neuper, C. (2012) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2012.01.014 · Cited by 125 · see citing works
Self-citationBetter than random: A closer look on BCI results
Muller-Putz, G. R., Scherer, R., Brunner, C., Leeb, R., & Pfurtscheller, G. (2007) · Cited by 117 · see citing works
Self-citationAn 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
Ten Simple Rules for Effective Statistical Practice
Kass, R. E., Caffo, B. S., Davidian, M., Meng, X.-L., Yu, B., & Reid, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004961 · Cited by 113 · see citing works
Seno, B. D., Matteucci, M., & Mainardi, L. T. (2009) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2009.2032642 · Cited by 109 · see citing works
On the control of brain-computer interfaces by users with cerebral palsy
Daly, I. P., Billinger, M., Laparra-Hernández, J., Fabio Aloise, A., García, M. L., Faller, J., Scherer, R., & Muller-Putz, G. R. (2013) · Clinical Neurophysiology · doi:10.1016/j.clinph.2013.02.118 · Cited by 102 · see citing works
Self-citationBarriers to and mediators of brain–computer interface user acceptance: focus group findings
Blain‐Moraes, S., Schaff, R., Gruis, K. L., Huggins, J. E., & Wren, P. A. (2012) · Ergonomics · doi:10.1080/00140139.2012.661082 · Cited by 102 · see citing works
Electroencephalography ( EEG )‐Based Brain–Computer Interfaces
Lotte, F., Bougrain, L., & Clerc, M. (2015) · Wiley eBooks · doi:10.1002/047134608x.w8278 · Cited by 102 · see citing works
Self-citationGuide to Brain-Computer Music Interfacing
(2014) · doi:10.1007/978-1-4471-6584-2 · Cited by 100 · see citing works
A portable auditory P300 brain–computer interface with directional cues
Käthner, I., Ruf, C. A., Pasqualotto, E., Braun, C., Birbaumer, N., & Halder, S. (2012) · Clinical Neurophysiology · doi:10.1016/j.clinph.2012.08.006 · Cited by 98 · see citing works
Antelis, J. M., Montesano, L., Ramos‐Murguialday, A., Birbaumer, N., & Mínguez, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061976 · Cited by 98 · see citing works
Huggins, J. E., Moinuddin, A. A., Chiodo, A. E., & Wren, P. A. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2014.05.028 · Cited by 97 · see citing works
Gamma band activity associated with BCI performance: simultaneous MEG/EEG study
Ahn, M., Ahn, S., Hong, J. H., Cho, H., Kim, K., Kim, B. S., Chang, J. W., & Jun, S. C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00848 · Cited by 97 · see citing works
Effects of training and motivation on auditory P300 brain-computer interface performance
Baykara, E., Ruf, C. A., Fioravanti, C., Käthner, I., Simon, N., Kleih, S. C., Kübler, A., & Halder, S. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.04.054 · Cited by 91 · see citing works
Self-citationRiccio, A., Leotta, F., Bianchi, L., Aloise, F., Zickler, C., Hoogerwerf, E.-J., Kübler, A., Donatella Mattia, A., & Febo Cincotti, A. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025028 · Cited by 89 · see citing works
Friedrich, E. V. C., Neuper, C., & Scherer, R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076214 · Cited by 88 · see citing works
Self-citationTrue Zero-Training Brain-Computer Interfacing – An Online Study
Kindermans, P.-J., Schreuder, M., Schrauwen, B., Müller, K., & Tangermann, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102504 · Cited by 83 · see citing works
Training locked-in patients: a challenge for the use of brain-computer interfaces
Neumann, N., & Kübler, A. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814431 · Cited by 82 · see citing works
Scherer, R., Faller, J., Friedrich, E. V. C., Opisso, E., Costa, U. M., Kübler, A., & Muller-Putz, G. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123727 · Cited by 79 · see citing works
Self-citationPrediction of SSVEP-based BCI performance by the resting-state EEG network
Zhang, Y., Xu, P., Guo, D., & Yao, D. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/6/066017 · Cited by 67 · see citing works
The effect of multimodal and enriched feedback on SMR-BCI performance
Sollfrank, T., Ramsay, A., Perdikis, S., Williamson, J. H., Murray‐Smith, R., Leeb, R., Millán, J. D. R., & Kübler, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.06.004 · Cited by 63 · see citing works
Kübler, A., Holz, E. M., Sellers, E. W., & Vaughan, T. M. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2014.03.036 · Cited by 63 · see citing works
Bernabeu, M., Laxe, S., López, R., Stucki, G., Ward, A. B., Barnes, M., Kostanjsek, N. F. I., Reed, G. M., Tate, R. L., Whyte, J., et al. (2009) · Neurorehabilitation and neural repair · doi:10.1177/1545968308328725 · Cited by 56 · see citing works
A co-adaptive brain-computer interface for end users with severe motor impairment
Faller, J., Scherer, R., Costa, U., Opisso, E., Medina, J., & Müller-Putz, G. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101168 · Cited by 55 · see citing works
Self-citationComparing metrics to evaluate performance of regression methods for decoding of neural signals
Spüler, M., Sarasola-Sanz, A., Birbaumer, N., Rosenstiel, W., & Ramos‐Murguialday, A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318553 · Cited by 55 · see citing works
Effects of resting heart rate variability on performance in the P300 brain-computer interface
Kaufmann, T., Vögele, C., Sütterlin, S., Lukito, S., & Kübler, A. (2011) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2011.11.018 · Cited by 54 · see citing works
Schwarzkopf, S. R., Ewert, T., Dreinhöfer, K., Cieza, A., & Stucki, G. (2008) · Clinical Rheumatology · doi:10.1007/s10067-008-0916-y · Cited by 54 · see citing works
Brain–computer interfacing: more than the sum of its parts
Scherer, R., Faller, J., Balderas, D., Friedrich, E. V. C., Pröll, M., Allison, B. Z., & Muller-Putz, G. R. (2012) · Soft Computing · doi:10.1007/s00500-012-0895-4 · Cited by 52 · see citing works
Self-citationThought-based row-column scanning communication board for individuals with cerebral palsy
Scherer, R., Billinger, M., Wagner, J., Schwarz, A., Hettich, D. T., Bolinger, E., García, M. L., Navarro, J. J., & Muller-Putz, G. R. (2015) · Annals of Physical and Rehabilitation Medicine · doi:10.1016/j.rehab.2014.11.005 · Cited by 48 · see citing works
Self-citationPeters, B., Bieker, G., Heckman, S. M., Huggins, J. E., Wolf, C., Zeitlin, D., & Fried-Oken, M. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2014.03.037 · Cited by 44 · see citing works
Self-citationLong-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance
Saeedi, S., Chavarriaga, R., & Millán, J. D. R. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2645681 · Cited by 43 · see citing works
Self-citationPsychological Factors Influencing Brain-Computer Interface (BCI) Performance
Kleih, S. C., & Kübler, A. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.554 · Cited by 41 · see citing works
Self-citationEvaluation Criteria for BCI Research
Dornhege, G., Millán, J. D. R., Hinterberger, T., McFarland, D. J., & Müller, K. (2007) · Cited by 41 · see citing works
Nijboer, F., Bos, D. P.-O., Blokland, Y., van Wijk, R., & Farquhar, J. D. R. (2014) · Brain-Computer Interfaces · doi:10.1080/2326263x.2013.877210 · Cited by 36 · 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
Self-citationSoliciting BCI user experience feedback from people with severe speech and physical impairments
Peters, B., Mooney, A. R., Oken, B., & Fried-Oken, M. B. (2016) · Brain-Computer Interfaces · doi:10.1080/2326263x.2015.1138056 · Cited by 27 · see citing works
Self-citationXu, R., Jiang, N., Mrachacz‐Kersting, N., Dremstrup, K., & Farina, D. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00527 · Cited by 25 · see citing works
A general method for assessing brain–computer interface performance and its limitations
Hill, N. J., Häuser, A.-K., & Schalk, G. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026018 · Cited by 22 · see citing works
A clinical screening protocol for the RSVP Keyboard brain–computer interface
Fried-Oken, M. B., Mooney, A. R., Peters, B., & Oken, B. (2013) · Disability and Rehabilitation Assistive Technology · doi:10.3109/17483107.2013.836684 · Cited by 22 · see citing works
Self-citationInitial constructs for patient-centered outcome measures to evaluate brain–computer interfaces
Andresen, E. M., Fried-Oken, M. B., Peters, B., & Patrick, D. L. (2015) · Disability and Rehabilitation Assistive Technology · doi:10.3109/17483107.2015.1027298 · Cited by 19 · see citing works
Self-citationQuitadamo, L. R., Abbafati, M., Cardarilli, G. C., Donatella Mattia, A., Febo Cincotti, A., Babiloni, F., Marciani, M. G., & Bianchi, L. (2011) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2011.10.010 · Cited by 19 · see citing works
Game-Based BCI Training: Interactive Design for Individuals with Cerebral Palsy
Scherer, R., Schwarz, A., Muller-Putz, G. R., Pammer‐Schindler, V., & García, M. L. (2015) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2015.551 · Cited by 17 · see citing works
Self-citationSubject-oriented training for motor imagery brain-computer interfaces
Perdikis, S., Leeb, R., & Millán, J. D. R. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943826 · Cited by 15 · see citing works
A public data hub for benchmarking common brain–computer interface algorithms
Zander, T. O., Ihme, K. A., Gärtner, M., & Rötting, M. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025021 · Cited by 13 · see citing works
Thought-based interaction with the physical world
Scherer, R., & Pfurtscheller, G. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.08.004 · Cited by 12 · see citing works
Self-citationLets play Tic-Tac-Toe: A Brain-Computer Interface case study in cerebral palsy
Scherer, R., Schwarz, A., Muller-Putz, G. R., Pammer‐Schindler, V., & García, M. L. (2016) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2016.7844815 · Cited by 12 · see citing works
Self-citationWhy and How to Use Intelligent Tutoring Systems to Adapt MI-BCI Training to Each User.
Jeunet, C., N’Kaoua, B., Nkambou, R., & Lotte, F. (2020) · HAL · doi:10.3217/978-3-85125-467-9-121 · Cited by 5 · see citing works
Self-citationScherer, R., Faller, J., Opisso, E., Costa, U. M., Steyrl, D., & Muller-Putz, G. R. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318858 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
1 of the 121 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- [33]Halder S Furdea A Varkuti B Auditory standard oddball and visual P300 brain-computer interface performance International Journal of Bioelectromagnetism 2011 13 1 5 6
- [36]Vukovic A 2010 Motor imagery questionnaire as a method to detect BCI illiteracy Paper presented at: 2010 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL 2010) 2010 7–10 Nov
- [48]Scherer R Wagner J Billinger M Augmenting communication, emotion expression and interaction capabilities of individuals with cerebral palsy Paper presented at: 2014 6th International Brain-Computer Interface Conference 2014 Graz, Austria 312 315 10.3217/978-3-85125-378-8-78 · DOI
- [57]Lotte F Jeunet C Towards Improved BCI based on Human Learning Principles Paper presented at: 3rd International Brain-Computer Interfaces Winter Conference 2015
- [77]Kobler R Scherer R Restricted Boltzmann Machines in Sensory Motor Rhythm Brain-Computer Interfacing: A Study on Inter-Subject Transfer and Co-Adaptation Paper presented at: 2016 IEEE International Conference on Systems, Man and Cybernetics (SMC) 2016 10 9–12 Budapest, Hungary
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
Self-citationSignal processing techniques for motor imagery brain computer interface: A review
Aggarwal, S., & Chugh, N. (2019) · Array · doi:10.1016/j.array.2019.100003 · Cited by 224 · see citing works
The 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
Light, J., McNaughton, D., Beukelman, D. R., Fager, S. K., Fried-Oken, M. B., Jakobs, T., & Jakobs, E. (2019) · Augmentative and Alternative Communication · doi:10.1080/07434618.2018.1556732 · Cited by 159 · see citing works
Self-citationCritiquing the Concept of BCI Illiteracy
Thompson, M. C. (2018) · Science and Engineering Ethics · doi:10.1007/s11948-018-0061-1 · Cited by 157 · 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
Self-citationKoch Fager, S., Fried-Oken, M., Jakobs, T., & Beukelman, D. R. (2019) · Augmentative and Alternative Communication · doi:10.1080/07434618.2018.1556730 · Cited by 107 · see citing works
Self-citationŠkola, F., Tinková, S., & Liarokapis, F. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00329 · Cited by 102 · see citing works
Brain-Machine Interfaces: A Tale of Two Learners
Perdikis, S., & Millán, J. D. R. (2020) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2019.2958200 · Cited by 87 · see citing works
Embodied VR environment facilitates motor imagery brain–computer interface training
Škola, F., & Liarokapis, F. (2018) · Computers & Graphics · doi:10.1016/j.cag.2018.05.024 · Cited by 86 · see citing works
Defining 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
Self-citationChallenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation
Simon, C., Bolton, D. A. E., Kennedy, N. C., Soekadar, S. R., & Ruddy, K. L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.699428 · Cited by 71 · see citing works
Tonin, L., Bauer, F. C., & Millán, J. D. R. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2943072 · Cited by 64 · see citing works
Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training
Benaroch, C., Sadatnejad, K., Roc, A., Appriou, A., Monseigne, T., Pramij, S., Mladenović, J., Pillette, L., Jeunet, C., & Lotte, F. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.635653 · Cited by 51 · see citing works
Self-citationLearning to control a BMI-driven wheelchair for people with severe tetraplegia
Tonin, L., Perdikis, S., Kuzu, T. D., Pardo, J., Orset, B., Lee, K., Aach, M., Schildhauer, T. A., Martínez, R., & Millán, J. D. R. (2022) · iScience · doi:10.1016/j.isci.2022.105418 · Cited by 50 · see citing works
Effects of Gamification in BCI Functional Rehabilitation
de Castro-Cros, M., Sebastián-Romagosa, M., Rodríguez-Serrano, J., Opisso, E., Ochoa, M., Ortner, R., Guger, C., & Tost, D. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00882 · Cited by 48 · see citing works
Transfer learning promotes acquisition of individual BCI skills
Kumar, S., Alawieh, H., Rácz, F. S., Fakhreddine, R., & Millán, J. D. R. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae076 · Cited by 45 · see citing works
Peters, B., Eddy, B. S., Galvin-McLaughlin, D., Betz, G., Oken, B., & Fried-Oken, M. B. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.952380 · Cited by 40 · see citing works
Self-citationGrand Challenges in Neurotechnology and System Neuroergonomics
Fairclough, S. H., & Lotte, F. (2020) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2020.602504 · Cited by 40 · see citing works
Self-citationOmedes, J., Schwarz, A., Müller-Putz, G., & Montesano, L. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aac1a1 · Cited by 35 · see citing works
Brain-computer interface paradigms and neural coding
Tai, P., Ding, P., Wang, F., Gong, A., Li, T., Zhao, L., Su, L., & Fu, Y. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1345961 · Cited by 35 · see citing works
Clinician awareness of brain computer interfaces: a Canadian national survey
Létourneau, S., Zewdie, E., Jadavji, Z., Andersen, J. C., Burkholder, L. M., & Kirton, A. (2020) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-019-0624-7 · Cited by 33 · see citing works
Harnessing Prefrontal Cognitive Signals for Brain–Machine Interfaces
Min, B., Chavarriaga, R., & Millán, J. D. R. (2017) · Trends in biotechnology · doi:10.1016/j.tibtech.2017.03.008 · Cited by 34 · see citing works
Self-citationHuggins, J. E., Guger, C., Ziat, M., Zander, T. O., Taylor, D., Tangermann, M., Soria‐Frisch, A., Simeral, J. D., Scherer, R., Rupp, R., et al. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1275488 · Cited by 30 · see citing works
Self-citationMotor 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-citationBrain computer interface systems using non-invasive electroencephalogram signal: A literature review
Kalagi, S. L., Machado, J., Carvalho, V. H., Soares, F., & Matos, D. (2017) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice.2017.8280071 · Cited by 27 · see citing works
EEG Waveform Analysis of P300 ERP with Applications to Brain Computer Interfaces
Ramele, R., Villar, A. J., & Santos, J. M. (2018) · Brain Sciences · doi:10.3390/brainsci8110199 · Cited by 27 · see citing works
Hearing the needs of clinical users
Kübler, A., Nijboer, F., & Kleih, S. C. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00026-3 · Cited by 25 · see citing works
Self-citationA multi-modal modified feedback self-paced BCI to control the gait of an avatar
Alchalabi, B., Faubert, J., & Labbé, D. R. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abee51 · Cited by 25 · see citing works
Noninvasive EEG-Based Intelligent Mobile Robots: A Systematic Review
Li, H., Li, X., & Millán, J. D. R. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3441055 · Cited by 24 · see citing works
An Online Data Visualization Feedback Protocol for Motor Imagery-Based BCI Training
Duan, X., Xie, S., Xie, X., Obermayer, K., Cui, Y., & Wang, Z. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.625983 · Cited by 24 · see citing works
Chen, Y., Wang, F., Li, T., Zhao, L., Gong, A., Nan, W., Ding, P., & Fu, Y. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1449208 · Cited by 24 · see citing works
BciPy: brain–computer interface software in Python
Memmott, T., Koçanaoğulları, A., Lawhead, M., Klee, D., Dudy, S., Fried-Oken, M. B., & Oken, B. (2021) · Brain-Computer Interfaces · doi:10.1080/2326263x.2021.1878727 · Cited by 23 · see citing works
Self-citationNeural correlates of user learning during long-term BCI training for the Cybathlon competition
Tortora, S., Beraldo, G., Bettella, F., Formaggio, E., Rubega, M., Felice, A. D., Masiero, S., Ruggero Carli, A., Petrone, N., Menegatti, E., et al. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01047-x · Cited by 23 · see citing works
Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery
Lyu, X., Ding, P., Li, S., Dong, Y., Su, L., Zhao, L., Gong, A., & Fu, Y. (2022) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09808-z · Cited by 22 · see citing works
Pitt, K. M., & Brumberg, J. S. (2021) · Assistive Technology · doi:10.1080/10400435.2021.1949405 · Cited by 20 · see citing works
EEG Based Motor Imagery Classification Using Instantaneous Phase Difference Sequence
Kumar, S., Reddy, T. K., & Behera, L. (2018) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2018.00094 · Cited by 20 · see citing works
Han, Y., Ziebell, P., Riccio, A., & Halder, S. (2022) · Brain-Computer Interfaces · doi:10.1080/2326263x.2022.2041294 · Cited by 19 · see citing works
General principles of machine learning for brain-computer interfacing
Iturrate, I., Chavarriaga, R., & Millán, J. D. R. (2020) · Handbook of clinical neurology · doi:10.1016/b978-0-444-63934-9.00023-8 · Cited by 22 · see citing works
Self-citationWorkshops of the eighth international brain–computer interface meeting: BCIs: the next frontier
Huggins, J. E., Krusienski, D. J., Vansteensel, M. J., Valeriani, D., Thelen, A., Stavisky, S. D., Norton, J. J. S., Nijholt, A., Muller-Putz, G. R., Kosmyna, N., et al. (2022) · Brain-Computer Interfaces · doi:10.1080/2326263x.2021.2009654 · Cited by 17 · see citing works
Self-citationBorgheai, S. B., Zisk, A. H., McLinden, J., McIntyre, J., Sadjadi, R., & Shahriari, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107658 · Cited by 17 · see citing works
HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury
Höller, Y., Thomschewski, A. C., Uhl, A., Bathke, A. C., Nardone, R., Leis, S., Trinka, E., & Höller, P. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00955 · Cited by 17 · see citing works
User Adaptation to Closed-Loop Decoding of Motor Imagery Termination
Orset, B., Lee, K., Chavarriaga, R., & Millán, J. D. R. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.3001981 · Cited by 17 · see citing works
Self-citationRobinson, N., Chouhan, T. S., Mihelj, E., Kratka, P., Debraine, F., Wenderoth, N., Guan, C., & Lehner, R. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.648275 · Cited by 16 · see citing works
Pitt, K. M., & Dietz, A. R. (2021) · American Journal of Speech-Language Pathology · doi:10.1044/2021_ajslp-21-00152 · Cited by 17 · see citing works
Ahmed, B., Khan, S., Lim, H., & Ku, J. H. (2025) · Electronics · doi:10.3390/electronics14030491 · Cited by 17 · see citing works
Ziebell, P., Stümpfig, J., Eidel, M., Kleih, S. C., Kübler, A., Latoschik, M. E., & Halder, S. (2020) · Scientific Reports · doi:10.1038/s41598-020-67887-6 · Cited by 16 · see citing works
Self-citationPost-Adaptation Effects in a Motor Imagery Brain-Computer Interface Online Coadaptive Paradigm
Cunha, J. D., Perdikis, S., Halder, S., & Scherer, R. (2021) · IEEE Access · doi:10.1109/access.2021.3064226 · Cited by 16 · see citing works
Self-citationPitt, K. M., Brumberg, J. S., & Pitt, A. R. (2019) · PubMed · Cited by 16 · see citing works
Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performance
Śliwowski, M., Martin, M., Souloumiac, A., Blanchart, P., & Aksenova, T. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1111645 · Cited by 17 · see citing works
Pitt, K. M., McKelvey, M. L., & Weissling, K. S. E. (2022) · Brain-Computer Interfaces · doi:10.1080/2326263x.2022.2057758 · Cited by 15 · see citing works
Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments
Alonso-Valerdi, L. M., & Rodrigo, M.-G. V. (2017) · Computational Intelligence and Neuroscience · doi:10.1155/2017/6076913 · Cited by 15 · see citing works
Lotte, F., Jeunet, C., Chavarriaga, R., Bougrain, L., Thompson, D., Scherer, R., Mowla, M., Kübler, A., Grosse-Wentrup, M., Dijkstra, K., et al. (2019) · Brain-Computer Interfaces · doi:10.1080/2326263x.2019.1697143 · Cited by 11 · see citing works
Self-citationLearning 2-in-1: Towards Integrated EEG-fMRI-NeurofeedbackPreprint
Perronnet, L., Lécuyer, A., Mano, M., Fleury, M., Lioi, G., Cury, C., Clerc, M., Lotte, F., & Barillot, C. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/397729 · Cited by 15 · see citing works
Self-citationEEG-based Endogenous Online Co-Adaptive Brain-Computer Interfaces: Strategy for Success?
Scherer, R., Faller, J., Sajda, P., & Vidaurre, C. (2018) · Computer Science and Electronic Engineering Conference · doi:10.1109/ceec.2018.8674198 · Cited by 13 · see citing works
Self-citationLu, X., Ding, P., Li, S., Gong, A., Zhao, L., Qian, Q., Su, L., & Fu, Y. (2021) · PubMed Central · doi:10.7507/1001-5515.202101093 · Cited by 12 · see citing works
Zhang, X., Hui, L., Wei, L., Song, F., & Hu, F. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18147567 · Cited by 13 · see citing works
Brain-Computer Interfaces for Human Augmentation
Poli, R., Valeriani, D., & Cinel, C. (2019) · MDPI eBooks · doi:10.3390/books978-3-03921-907-0 · Cited by 11 · see citing works
An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces
Papadopoulos, S., Bonaiuto, J. J., & Mattout, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.824759 · Cited by 10 · see citing works
Carrere, L. C., Escher, L., Gentiletti, G. G., & Tabernig, C. B. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-021-00164-1 · Cited by 9 · see citing works
Karlsson, P., Orlandi, S., Zhao, H., & McEwan, A. L. (2021) · Springer eBooks · doi:10.1007/978-981-16-5324-7_2 · Cited by 10 · see citing works
Collazos-Huertas, D. F., Álvarez-Meza, A. M., & Castellanos-Dominguez, G. (2022) · Applied Sciences · doi:10.3390/app12031695 · Cited by 9 · see citing works
Beta bursts question the ruling power for brain–computer interfaces
Papadopoulos, S., Szul, M. J., Congedo, M., Bonaiuto, J. J., & Mattout, J. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad19ea · Cited by 8 · see citing works
The sixth international brain–computer interface meeting: advances in basic and clinical research
Huggins, J. E., Muller-Putz, G. R., & Wolpaw, J. R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2017.1328211 · Cited by 8 · see citing works
Fried-Oken, M. B., Kinsella, M., Stevens, I., & Klein, E. P. (2023) · Brain-Computer Interfaces · doi:10.1080/2326263x.2023.2283345 · Cited by 7 · see citing works
Self-citationVirtual Reality Brain–Computer Interfacing and the Role of Cognitive Skills
Hadjiaros, M., Shimi, A. G., Avraamides, M. N., Neokleous, K., & Pattichis, C. S. (2024) · IEEE Access · doi:10.1109/access.2024.3426620 · Cited by 7 · see citing works
Demirel, B. U., Dogan, A. H., Rossie, J., Moebus, M., & Holz, C. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3549132 · Cited by 7 · see citing works
Using Robust Principal Component Analysis to Reduce EEG Intra-Trial Variability
Jao, P.-K., Chavarriaga, R., & Millán, J. D. R. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8512687 · Cited by 7 · see citing works
Self-citationPitt, K. M., McKelvey, M. L., Weissling, K. S. E., & Thiessen, A. (2024) · International Journal of Speech-Language Pathology · doi:10.1080/17549507.2024.2363939 · Cited by 5 · see citing works
Floreani, E. D., Kelly, D., Rowley, D., Irvine, B., Kinney‐Lang, E., & Kirton, A. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871105 · Cited by 5 · see citing works
Histogram of Gradient Orientations of Signal Plots Applied to P300 Detection
Ramele, R., Villar, A. J., & Santos, J. M. (2019) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2019.00043 · Cited by 5 · see citing works
Hougaard, B. I., Knoche, H. O., Kristensen, M. S., & Jochumsen, M. (2025) · Sensors · doi:10.3390/s25092742 · Cited by 4 · see citing works
Muller, C., Prampart, T., Bannier, É., Corouge, I., & Maurel, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0331177 · Cited by 4 · see citing works
The identity of the protagonist in brain-computer interfaces: A user-centered design approach
Mercado, V., & Alonso-Valerdi, L. M. (2018) · International Conference on Electronics, Communications, and Computers · doi:10.1109/conielecomp.2018.8327174 · Cited by 4 · see citing works
Brain–Computer Interfacing with Interactive Systems—Case Study 2
Vourvopoulos, A., Niforatos, E., Badia, S. B. I., & Liarokapis, F. (2021) · ACM eBooks · doi:10.1145/3447404.3447418 · Cited by 4 · see citing works
Thought-based interaction: Same data, same methods, different results?
Scherer, R. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000190 · Cited by 2 · see citing works
Self-citationDo User-Centered Designed Paradigms for BCIs improve the Modulation of EEG Signals?
Mercado, V., Alonso-Valerdi, L. M., Salas‐Garza, M., Lopez-Montoya, A., Garza-Ibarra, E., & Chávez-Madero, C. (2021) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner49283.2021.9441423 · Cited by 3 · see citing works
Closed-loop motor imagery EEG simulation for brain-computer interfaces
Shin, H., Suma, D., & He, B. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.951591 · Cited by 4 · see citing works
Alternative Access for Adults Who Rely on Augmentative and Alternative Communication
Fager, S. K. (2018) · Perspectives of the ASHA Special Interest Groups · doi:10.1044/persp3.sig12.6 · Cited by 3 · see citing works
Roc, A., Kolodzienski, L., Dreyer, P., Appriou, A., Monseigne, T., & Lotte, F. (2025) · Brain Research · doi:10.1016/j.brainres.2025.150085 · Cited by 2 · see citing works
Self-citationUpdating BCI paradigms: Why to design in terms of the user?
Alonso-Valerdi, L. M., & Mercado, V. (2021) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner49283.2021.9441337 · Cited by 2 · see citing works
A Review of Brain-Computer Interface (BCI) System: Advancement and Applications
Gupta, B. K., Koirala, T. K., Rai, J., Panda, B., & Bhoi, A. K. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-38281-9_9 · Cited by 2 · see citing works
Hirano, R., & Watanabe, K. (2023) · ACM Symposium on Virtual Reality Software and Technology (VRST) · doi:10.1145/3611659.3616898 · Cited by 2 · see citing works
Electroencephalography and Brain–Computer Interfaces
Muller-Putz, G. R., & Wriessnegger, S. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-68545-4_3 · Cited by 2 · see citing works
Designing an online BCI forum: insights from researchers and end-users
Ziebell, P., Modde, A., Roland, E., Eidel, M., Vansteensel, M. J., Mrachacz‐Kersting, N., Vaughan, T. M., & Kübler, A. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adaf57 · Cited by 1 · see citing works
What if I had a third arm? An EEG study of a supernumerary BCI systemPreprint
Riascos, J. A., Villa, S., Maciel, A., Nedel, L., & Barone, D. A. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/817205 · Cited by 1 · see citing works
Ziebell, P., Stümpfig, J., Kleih, S. C., Latoschik, M. E., Kübler, A., & Halder, S. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-682-6-46
Self-citationPitt, K. M., McKelvey, M. L., Weissling, K. S. E., & Klein, B. (2020) · UNL Digital Commons · doi:10.32873/unl.dc.oth.004
Ravindranathan, R., Usha, S., Tommy, R., & George, S. R. (2024) · Journal of Intellectual Disability - Diagnosis and Treatment · doi:10.6000/2292-2598.2024.12.04.6
Integrating Mind-Controlled Navigation Into a Virtual Reality Training System
Gałązkiewicz, A., & Walczak, K. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97763-3_17
MotiVE BCI: motivation models including valence and expectancy in brain–computer interface use
Kleih, S. C. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1681683
Self-citationNguyen, T. D. H., & Truong, D. H. (2026) · Tropical Journal of Natural Product Research · doi:10.26538/tjnpr/v10i9.51
Human Factors in Brain–Computer Interfaces: Boundaries, Key Issues, and Methodological Approaches
Yan, L., Li, T., Zhao, L., Gong, A., Hao, F., Zuo, W., & Fu, Y. (2026) · Brain Sciences · doi:10.3390/brainsci16101066
Pitt, K. M., Sawall, J., Butler, E., Bubak, A., & Behboodi, A. (2026) · Journal of Speech Language and Hearing Research · doi:10.1044/2026_jslhr-26-00267
- Authors
- Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Oregon Health & Science University (US)
- University of Würzburg (DE)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Graz University of Technology (AT)
- Corresponding author
- Chavarriaga, Ricardo
- Venue
- Brain-Computer Interfaces
- Publisher
- Informa UK Limited
- Published
- 2016-12-30
- Volume
- 4
- Issue
- 1-2
- Pages
- 60-73
- ISSN
- 2326-2621 (ISSN-L)
- 2326-263X
- PMC
- PMC5884128
- OpenAlex
- W2567013686
- Cited by
- 97
- References
- 121
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 9 · 2023: 5 · 2022: 11 · 2021: 18 · 2020: 11 · 2019: 11 · 2018: 15 · 2017: 5
- FWCI
- 6.58
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain-Computer Interfaces (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- Agence Nationale de la Recherche (ANR-15-CE23-0013-01)
- National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR grant #90RE5017)
- National Institutes of Health (DC009834-01)
- Land Steiermark project rE(EG)map! (ABT08-31142/2014-10)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (NCCR-Robotics)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- assistive technology, long-term usability, publication bias, BCI performance, communication devices, motor impairment
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
