Neurophysiological predictor of SMR-based BCI performance
Abstract
Brain-computer interfaces (BCIs) allow a user to control a computer application by brain activity as measured, e.g., by electroencephalography (EEG). After about 30years of BCI research, the success of control that is achieved by means of a BCI system still greatly varies between subjects. For about 20% of potential users the obtained accuracy does not reach the level criterion, meaning that BCI control is not accurate enough to control an application. The determination of factors that may serve to predict BCI performance, and the development of methods to quantify a predictor value from psychological and/or physiological data serve two purposes: a better understanding of the 'BCI-illiteracy phenomenon', and avoidance of a costly and eventually frustrating training procedure for participants who might not obtain BCI control. Furthermore, such predictors may lead to approaches to antagonize BCI illiteracy. Here, we propose a neurophysiological predictor of BCI performance which can be determined from a two minute recording of a 'relax with eyes open' condition using two Laplacian EEG channels. A correlation of r=0.53 between the proposed predictor and BCI feedback performance was obtained on a large data base with N=80 BCI-naive participants in their first session with the Berlin brain-computer interface (BBCI) system which operates on modulations of sensory motor rhythms (SMRs).
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EEG signals in BCI exhibit great cross-subject variability.
“The electroencephalogram (EEG) signal in the brain–computer interface (BCI) has suffered great cross-subject variability.”
From Transfer Learning Algorithm of P300-EEG Signal Based on XDAWN Spatial Filter and Riemannian Geometry Classifier (2020) · cites this paper as [9]
General concerns about non-universal usability, robustness, and the role of training raised by able-bodied user studies are even more pressing with regard to end-user populations.
“As a result, the general concerns about the non-universal usability, robustness, and, especially, the role of training raised by able-bodied user studies [3–7] are even more pressing with regard to end-user populations.”
From The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users (2018) · cites this paper as [5]
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
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,485 · see citing works
Self-citationOptimizing 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 7× · see citing works
“Details on the heuristics underlying the selection of frequency band and time interval can be found in Blankertz et al. (2008b).”
InfluentialSelf-citationMu 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
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
Self-citationControl of a two-dimensional movement signal by a noninvasive brain-computer interface in humans
Wolpaw, J. R., & McFarland, D. J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403504101 · Cited by 1,512 · 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-citationToward 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-citationPfurtscheller, G., & Aranibar, A. (1979) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(79)90063-4 · Cited by 758 · cited 1× · see citing works
“Modulations of the µ-rhythm have been reported for different manipulations, e.g., motor activity, both actual and imagined (Jasper and Penfield (1949); Pfurtscheller and Aranibar (1979); Schnitzler et al. (1997)), as well as somatosensory stimulation (Nikouline et al. (2000)).”
BackgroundNeuper, 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
Jasper, H. H., & Penfield, W. G. (1949) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01062488 · Cited by 682 · see citing works
Human cortical oscillations: a neuromagnetic view through the skull
Hari, R. (1997) · Trends in Neurosciences · doi:10.1016/s0166-2236(96)10065-5 · Cited by 680 · see citing works
Dornhege, 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-citationBrain-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
Self-citationPatients 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
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-citationInvolvement of Primary Motor Cortex in Motor Imagery: A Neuromagnetic Study
Schnitzler, A., Salenius, S., Salmelin, R. H., Jousmäki, V., & Hari, R. (1997) · NeuroImage · doi:10.1006/nimg.1997.0286 · Cited by 434 · 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
Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI)
Guger, C., Ramoser, H., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895947 · Cited by 384 · 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-citationKumar, K. (2011) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00676_4.x · Cited by 345 · see citing works
The 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-citationNeuper, 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-citationBrickenkamp, R., & Zilmer, E. (1998) · PsycTESTS Dataset · doi:10.1037/t03299-000 · Cited by 268 · see citing works
Cortically coupled computer vision for rapid image search
Gerson, A. D., Parra, L. C., & Sajda, P. (2006) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2006.875550 · Cited by 258 · see citing works
Towards Zero Training for Brain-Computer Interfacing
Krauledat, M., Tangermann, M., Blankertz, B., & Müller, K. R. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002967 · Cited by 248 · see citing works
Self-citationJasper, H. H. (1938) · Archives of Neurology And Psychiatry · doi:10.1001/archneurpsyc.1938.02270010106010 · Cited by 215 · see citing works
Single 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
Self-citationBiofeedback of slow cortical potentials. I
Elbert, T. R., Rockstroh, B. S., Lutzenberger, W., & Birbaumer, N. (1980) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(80)90265-5 · Cited by 198 · 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
Kohlmorgen, J., Dornhege, G., Braun, M., Blankertz, B., Müller, K., Curio, G., Hagemann, K., Bruns, A., Schrauf, M., & Kincses, W. E. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7493.003.0031 · Cited by 179 · see citing works
Self-citationPredictability 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
Self-citationÜber das Elektrenkephalogramm des Menschen
Berger, H. (1933) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01814320 · Cited by 122 · see citing works
EEG-based communication and control: short-term role of feedback
McFarland, D. J., McCane, L. M., & Wolpaw, J. R. (1998) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.662615 · Cited by 121 · 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
Self-citationPhysiological 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
Predictors of successful self control during brain-computer communication
Neumann, N. (2003) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.74.8.1117 · Cited by 100 · see citing works
Nikulin, V. V., Linkenkaer‐Hansen, K., Wikström, H., Kesäniemi, M., Antonova, E. V., Ilmoniemi, R. J., & Huttunen, J. (2000) · Neuroscience Letters · doi:10.1016/s0304-3940(00)01562-7 · Cited by 83 · 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
Motor Cortex in Voluntary Movements
(2004) · Frontiers in neuroscience · doi:10.1201/9780203503584.supl · Cited by 67 · see citing works
Daum, I., Rockstroh, B. S., Birbaumer, N., Elbert, T. R., Canavan, A. G. M., & Lutzenberger, W. (1993) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.56.1.94 · Cited by 51 · 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
Learning and Self-Regulation of Slow Cortical Potentials in Older Adults
Kotchoubey, S. H. B. (2000) · Experimental Aging Research · doi:10.1080/036107300243669 · Cited by 18 · see citing works
Heart rate and heart-rate variability during hypnotic responding
Carras, P. (1994) · International Journal of Psychophysiology · doi:10.1016/0167-8760(94)90373-5 · Cited by 7 · see citing works
Flagged first, then by citations
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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
Closed-loop brain training: the science of neurofeedback
Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M. L., Rana, M., Oblak, E., et al. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.164 · Cited by 1,306 · see citing works
Riemannian Approaches in Brain-Computer Interfaces: A Review
Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420 · see citing works
The 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-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
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
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He, B., Baxter, B., Edelman, B. J., Cline, C. C., & Ye, W. W. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2407272 · Cited by 233 · see citing works
Learning to modulate one’s own brain activity: The effect of spontaneous mental strategies
Kober, S. E., Witte, M., Ninaus, M., Neuper, C., & Wood, G. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00695 · Cited by 225 · see citing works
Machine-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-citationJeunet, C., Jahanpour, E., & Lotte, F. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/3/036024 · Cited by 192 · 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
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
Co-adaptive calibration to improve BCI efficiency
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“Besides, some people cannot produce distinctive EEG signals [5], and it is difficult for them to focus on MI when moving.”
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“One of the main reasons hindering the widespread use of BCI systems is the variability of EEG signals [1,9].”
BackgroundHiremath, S. V., Chen, W., Wang, W., Foldes, S., Yang, Y., Tyler-Kabara, E. C., Collinger, J. L., & Boninger, M. L. (2015) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2015.00040 · Cited by 53 · see citing works
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- Authors
- Blankertz, B., Sannelli, C., Halder, S., Hammer, E. M., Kübler, A., Müller, K.-R., Curio, G., & Dickhaus, T.
- Affiliations
- Technische Universität Berlin (DE)
- University of Tübingen (DE)
- University of Würzburg (DE)
- Charité – Universitätsmedizin Berlin (DE)
- Corresponding author
- Dickhaus, Thorsten
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-07
- Volume
- 51
- Issue
- 4
- Pages
- 1303-1309
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2030729605
- Cited by
- 674
- References
- 46
- Citations by year
- 2026: 27 · 2025: 40 · 2024: 25 · 2023: 38 · 2022: 38 · 2021: 66 · 2020: 64 · 2019: 37 · 2018: 52 · 2017: 33 · 2016: 42 · 2015: 57 · 2014: 31 · 2013: 50 · 2012: 28
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (DFG) (MU 987/3-1, KU 1453-1)
- Bundesministerium für Bildung und Forschung (BMBF) (FKZ 01IB001A/B, 01GQ0850)
- FP7-ICT Programme of the European Community, under the PASCAL2 Network of Excellence (ICT-216886)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- brain-computer interface, sensory motor rhythm, BCI illiteracy, EEG
- MeSH terms
- Adult, Algorithms, Biofeedback, Psychology, Calibration, Computer Literacy, Cues, Data Interpretation, Statistical, Electroencephalography, Female, Hand, Humans, Functional Laterality, Male, Motor Cortex, Photic Stimulation, Psychomotor Performance, Somatosensory Cortex, User-Computer Interface, Artifacts
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