Autocalibration and recurrent adaptation: Towards a plug and play online ERD-BCI
Open accessAbstract
System calibration and user training are essential for operating motor imagery based brain-computer interface (BCI) systems. These steps are often unintuitive and tedious for the user, and do not necessarily lead to a satisfactory level of control. We present an Adaptive BCI framework that provides feedback after only minutes of autocalibration in a two-class BCI setup. During operation, the system recurrently reselects only one out of six predefined logarithmic bandpower features (10-13 and 16-24 Hz from Laplacian derivations over C3, Cz, and C4), specifically, the feature that exhibits maximum discriminability. The system then retrains a linear discriminant analysis classifier on all available data and updates the online paradigm with the new model. Every retraining step is preceded by an online outlier rejection. Operating the system requires no engineering knowledge other than connecting the user and starting the system. In a supporting study, ten out of twelve novice users reached a criterion level of above 70% accuracy in one to three sessions (10-80 min online time) of training, with a median accuracy of 80.2 ± 11.3% in the last session. We consider the presented system a positive first step towards fully autocalibrating motor imagery BCIs.
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 [38]
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 [38]
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
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328 · see citing works
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266 · see citing works
Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks
Pfurtscheller, G., Brunner, C., Schlögl, A., & Lopes, F. H. D. S. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.003 · Cited by 1,590 · see citing works
Pattern Recognition and Machine Learning
Burr, T. (2008) · Journal of the American Statistical Association · doi:10.1198/jasa.2008.s236 · Cited by 1,112 · see citing works
An on-line transformation of EEG scalp potentials into orthogonal source derivations
Hjorth, B. (1975) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(75)90056-5 · Cited by 1,014 · 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-citationJasper, H. H., & Penfield, W. G. (1949) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01062488 · Cited by 682 · see citing works
Neurophysiological 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
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface
Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T. M., Pawelzik, H., Schalk, G., McFarland, D. J., Birbaumer, N., & Wolpaw, J. R. (2005) · Neurology · doi:10.1212/01.wnl.0000158616.43002.6d · Cited by 539 · see citing works
How many people are able to operate an EEG-based brain-computer interface (BCI)?
Guger, C., Edlinger, G., Harkam, W., Niedermayer, I., & Pfurtscheller, G. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814481 · Cited by 526 · see citing works
Towards 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
Brain–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
Neuper, C., Scherer, R., Wriessnegger, S. C., & Pfurtscheller, G. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.11.015 · Cited by 421 · see citing works
Self-citationBetter than random: A closer look on BCI results
Mueller-Putz, G., Scherer, R., Brunner, C., Leeb, R., & Pfurtscheller, G. (2008) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 419 · see citing works
Self-citationAn Asynchronously Controlled EEG-Based Virtual Keyboard: Improvement of the Spelling Rate
Scherer, R., Müller, G., Neuper, C., Graimann, B., & Pfurtscheller, G. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2004.827062 · Cited by 340 · 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
Neuper, C., Müller, G. R., Kübler, A., Birbaumer, N., & Pfurtscheller, G. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00387-5 · Cited by 317 · see citing works
Self-citationRapid prototyping of an EEG-based brain-computer interface (BCI)
Guger, C., Schlögl, A., Neuper, C., Walterspacher, D., Strein, T., & Pfurtscheller, G. (2001) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/7333.918276 · Cited by 312 · see citing works
Self-citationGraz-BCI: state of the art and clinical applications
Pfurtscheller, G., Neuper, C., Müller, G., Obermaier, B., Krausz, G., Schlögl, A., Scherer, R., Graimann, B., Keinrath, C., Skliris, D., et al. (2003) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.814454 · Cited by 285 · see citing works
Self-citationNeuper, C., & Pfurtscheller, G. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00661-7 · Cited by 250 · see citing works
Self-citationMotor imagery and EEG-based control of spelling devices and neuroprostheses
Neuper, C., Muller-Putz, G. R., Scherer, R., & Pfurtscheller, G. (2006) · Progress in brain research · doi:10.1016/s0079-6123(06)59025-9 · Cited by 209 · see citing works
Self-citationMachine-learning-based coadaptive calibration for brain-computer interfaces
Vidaurre, C., Sannelli, C., Müller, K.-R., & Blankertz, B. (2011) · Neural Computation · doi:10.1162/neco_a_00089 · Cited by 207 · see citing works
Self-citationSingle Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes
Popescu, F., Fazli, S., Badower, Y., Blankertz, B., & Müller, K.-R. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000637 · Cited by 202 · 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-citationCo-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-citationCritical issues in state-of-the-art brain–computer interface signal processing
Krusienski, D. J., Grosse‐Wentrup, M., Galán, F., Coyle, D., Miller, K. J., Forney, E. M., & Anderson, C. W. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/2/025002 · Cited by 169 · 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-citationTemporal coding of brain patterns for direct limb control in humans
Mueller-Putz (2010) · Frontiers in Neuroscience · doi:10.3389/fnins.2010.00034 · Cited by 131 · see citing works
Better 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-citationBiased feedback in brain-computer interfaces
Barbero, Á., & Grosse‐Wentrup, M. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-34 · Cited by 111 · see citing works
Vidaurre, C., Schlögl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2005) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt.2005.049 · Cited by 32 · see citing works
Self-citation
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
Lotte, F., Larrue, F., & Mühl, C. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00568 · Cited by 307 · 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-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
Leeb, R., Perdikis, S., Tonin, L., Biasiucci, A., Tavella, M., Creatura, M., Molina, A., Al-Khodairy, A., Carlson, T., & Millán, J. D. R. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.08.004 · Cited by 166 · see citing works
Heading for new shores! Overcoming pitfalls in BCI design
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1263916 · Cited by 97 · see citing works
Self-citationA 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-citationContext-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
Statthaler, K., Schwarz, A., Steyrl, D., Kobler, R., Höller, M. K., Brandstetter, J., Hehenberger, L., Bigga, M., & Müller-Putz, G. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0344-9 · Cited by 24 · see citing works
Kobler, R. J., & Scherer, R. (2016) · 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2016.7844284 · Cited by 20 · see citing works
Self-citation
- Authors
- Faller, J., Vidaurre, C., Solis-Escalante, T., Neuper, C., & Scherer, R.
- Affiliations
- Graz University of Technology (AT)
- Technische Universität Berlin (DE)
- University of Graz (AT)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2012-05
- Volume
- 20
- Issue
- 3
- Pages
- 313-319
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2031130804
- Cited by
- 180
- References
- 33
- Citations by year
- 2026: 8 · 2025: 13 · 2024: 18 · 2023: 13 · 2022: 10 · 2021: 11 · 2020: 16 · 2019: 12 · 2018: 14 · 2017: 9 · 2016: 13 · 2015: 14 · 2014: 10 · 2013: 17 · 2012: 2
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- TECNALIA Publications (Fundación TECNALIA Research & Innovation) (submittedVersion, public-domain)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Keywords
- brain-computer interface, self-calibration, motor imagery, online retraining, linear discriminant analysis, outlier removal, plug and play, novice users
- MeSH terms
- Acoustic Stimulation, Adult, Alpha Rhythm, Brain, Calibration, Cortical Synchronization, Cues, Data Interpretation, Statistical, Electric Stimulation, Electroencephalography, Female, Humans, Learning, Male, Online Systems, Psychomotor Performance, User-Computer Interface, Young Adult
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
