The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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?
In the citing paper
“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]
In the citing paper
“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]
Citations
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
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
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
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×
“…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
Schäfer, J., & Strimmer, K. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1175 · Cited by 1,556
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
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
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
Sugiyama, M. (2007) · doi:10.5555/1248659.1248694 · Cited by 1,031
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
Research works printed but not linked: 2
- Blankertz B., 2010, Detecting mental states by machine learning techniques, 113
- Sugiyama M., 2007, Journal of Machine Learning Research, 8, 1027
Cited by
Sorted by citations
Items 101–200 of 206
Priming cross-session motor imagery classification with a universal deep domain adaptation framework
Zhang, X., Miao, Z., Menon, C., Zheng, Y., Zhao, M., & Ming, D. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126659 · Cited by 27
Arvaneh, M., Robertson, I. H., & Ward, T. E. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6945117 · Cited by 26
Brain-computer interfacing in discriminative and stationary subspaces
Samek, W., Müller, K. R., Kawanabe, M., & Vidaurre, C. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346563 · Cited by 26
Self-citationUsing NIRS as a predictor for EEG-based BCI performance
Fazli, S., Mehnert, J., Steinbrink, J. M., & Blankertz, B. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347095 · Cited by 25
Self-citationChen, 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
Automatic Survey-invariant Classification of Variable Stars
Benavente, P., Protopapas, P., & Pichara, K. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa7f2d · Cited by 24
Combining ERD and ERS features to create a system-paced BCI
Thomas, E. M., Fruitet, J., & Clerc, M. (2013) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.03.026 · Cited by 25
Choi, I., Kwon, G. H., Lee, S., & Nam, C. S. (2020) · Brain Sciences · doi:10.3390/brainsci10080512 · Cited by 24
Feedback for reinforcement learning based brain–machine interfaces using confidence metrics
Prins, N. W., Sanchez, J. C., & Prasad, A. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa6317 · Cited by 24
Neural 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., Carli, R., Petrone, N., Menegatti, E., et al. (2022) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-022-01047-x · Cited by 24
Building an adaptive interface via unsupervised tracking of latent manifolds
Rizzoglio, F., Casadio, M., Santis, D. D., & Mussa-Ivaldi, F. A. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.01.009 · Cited by 25
Challenges of neural interfaces for stroke motor rehabilitation
Vidaurre, C., Irastorza-Landa, N., Sarasola-Sanz, A., Insausti-Delgado, A., Ray, A. M., Bibián, C., Helmhold, F., Mahmoud, W. J., Ortego-Isasa, I., López‐Larraz, E., et al. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1070404 · Cited by 23
Self-citationA multi-agent control framework for co-adaptation in brain-computer interfaces
Merel, J., Fox, R., Jebara, T., & Paninski, L. (2013) · neural information processing systems · Cited by 21
Eyes-closed hybrid brain-computer interface employing frontal brain activation
Shin, J., Müller, K., & Hwang, H.-J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196359 · Cited by 21
Self-citationKobler, R. J., & Scherer, R. (2016) · 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · doi:10.1109/smc.2016.7844284 · Cited by 20
Resting-State-Based Spatial Filtering for an fNIRS-Based Motor Imagery Brain-Computer Interface
Zheng, Y., Zhang, D., Wang, L., Wang, Y., Deng, H. J., Zhang, S., Li, D., & Wang, D. (2019) · IEEE Access · doi:10.1109/access.2019.2936434 · Cited by 20
User Centred Design in BCI Development
Holz, E. M., Kaufmann, T., Desideri, L., Malavasi, M., Hoogerwerf, E.-J., & Kübler, A. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_8 · Cited by 19
Bringing BCI into everyday life: Motor imagery in a pseudo realistic environment
Brandl, S., Höhne, J., Müller, K., & Samek, W. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146600 · Cited by 20
Self-citationZeyl, T. J., & Chau, T. T. (2013) · Pattern Recognition Letters · doi:10.1016/j.patrec.2013.05.020 · Cited by 18
Fast mental states decoding in mixed reality
Massari, D. D., Pacheco, D., Malekshahi, R., Betella, A., Verschure, P. F. M. J., Birbaumer, N., & Carìa, A. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00415 · Cited by 18
Robust common spatial filters with a maxmin approach
Kawanabe, M., Vidaurre, C., Scholler, S., & Müller, K. R. (2009) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2009.5334786 · Cited by 17
Self-citationStephens, C. L., Dehais, F., Roy, R. N., Harrivel, A. R., Last, M. C., Kennedy, K. D., & Pope, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91470-1_9 · Cited by 17
Spüler, M., Nagel, S., & Rosenstiel, W. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280521 · Cited by 17
The Science of Neurofeedback: Learnability and Effects
Reiner, M., Gruzelier, J. H., Bamidis, P. D., & Auer, T. (2018) · Neuroscience · doi:10.1016/j.neuroscience.2018.04.024 · Cited by 20
Towards improvement of MI-BCI performance of subjects with BCI deficiency
Bamdadian, A., Guan, C., Ang, K. K., & Xu, J. (2015) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2015.7146549 · Cited by 16
On robust parameter estimation in brain–computer interfacing
Samek, W., Nakajima, S., Kawanabe, M., & Müller, K. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa8232 · Cited by 16
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 18
Investigating brief motor imagery for an ERD/ERS based BCI
Thomas, E. M., Fruitet, J., & Clerc, M. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346577 · Cited by 15
Yadav, H., & Maini, S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110937 · Cited by 16
Discriminative Learning of Propagation and Spatial Pattern for Motor Imagery EEG Analysis
Li, X., Zhang, H., Guan, C., Ong, S. H., Ang, K. K., & Pan, Y. (2013) · Neural Computation · doi:10.1162/neco_a_00500 · Cited by 14
EEG-Based Brain–Computer Interfaces
Pfurtscheller, G., Brunner, C., & Neuper, C. (2017) · Oxford University Press eBooks · doi:10.1093/med/9780190228484.003.0047 · Cited by 14
Lotte, F., & Jeunet, C. (2020) · HAL · doi:10.3217/978-3-85125-533-1-53 · Cited by 13
Botrel, L., Acqualagna, L., Blankertz, B., & Kübler, A. (2017) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2017.08.007 · Cited by 13
Self-citationDesigning Future BCIs: Beyond the Bit Rate
Quek, M., Höhne, J., Murray‐Smith, R., & Tangermann, M. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_9 · Cited by 13
EEG-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
Self-citationPascual, J., Velasco-Álvarez, F., Müller, K. R., & Vidaurre, C. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346663 · Cited by 11
Self-citationClassifying mental gestures with in-ear EEG
Merrill, N., Curran, M. T., Yang, J.-K., & Chuang, J. C.-I. (2016) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2016.7516246 · Cited by 11
Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding
Wang, J., Yao, L., & Wang, Y. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3591254 · Cited by 10
Common Spatial Pattern Patches: Online evaluation on BCI-naive users
Sannelli, C., Vidaurre, C., Müller, K.-R., & Blankertz, B. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347027 · Cited by 10
Self-citationToward an Open-Ended BCI: A User-Centered Coadaptive Design
Dhindsa, K., Carcone, D., & Becker, S. (2017) · Neural Computation · doi:10.1162/neco_a_01001 · Cited by 10
Lun, X., Zhang, Y., Zhu, M., Lian, Y., & Hou, Y. (2023) · Sensors · doi:10.3390/s23218893 · Cited by 9
Analysis of Minimal Channel Electroencephalography for Wearable Brain–Computer Interface
Suwannarat, A., Pan-ngum, S., & Israsena, P. (2024) · Electronics · doi:10.3390/electronics13030565 · Cited by 9
Wang, K.-J., Zheng, C. Y., Shidujaman, M., Wairagkar, M., & Mohr, M. V. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9283049 · Cited by 9
Abibullaev, B., An, J., Jin, S. H., & Moon, J.-I. (2013) · Measurement · doi:10.1016/j.measurement.2013.11.044 · Cited by 10
Automatic adaptation to oscillatory EEG activity in spinal cord injury and stroke patients
Faller, J., Vidaurre, C., Friedrich, L. A., Costa, U. M., Opisso, E., Medina-Casanovas, J., Neuper, C., Muller-Putz, G. R., & Scherer, R. (2012) · Cited by 7
Self-citationYang, H., Guan, C., Ang, K. K., Pan, Y., & Zhang, H. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6287943 · Cited by 7
MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM
Labib, F. E.-Z. M., Fouad, I. A., Mabrouk, M. S., & Sharawy, A. A. (2020) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237220500106 · Cited by 7
A supervised recalibration protocol for unbiased BCI
Perdikis, S., Tavella, M., Leeb, R., Chavarriaga, R., & Millán, J. D. R. (2011) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 6
Wojcikiewicz, W., Vidaurre, C., & Kawanabe, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21222-2_5 · Cited by 6
Self-citationLi, Z., Jiang, Y., Duan, L. F., & Zhu, C.-Z. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa71c0 · Cited by 6
Eliseyev, A., & Aksenova, T. (2019) · Brain-Computer Interfaces · doi:10.1080/2326263x.2019.1651570 · Cited by 6
Ästrand, E., Plantin, J., Palmcrantz, S. M., & Tidare, J. (2021) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner49283.2021.9441076 · Cited by 6
Advances in high-dimensional covariance matrix estimation
Bartz, D. (2016) · DepositOnce · doi:10.14279/depositonce-5038 · Cited by 6
Dhindsa, K., Carcone, D., & Becker, S. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58625-0_23 · Cited by 5
Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning
Porbadnigk, A. K., Görnitz, N., Kloft, M., & Müller, K. (2013) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2013.7.2.112 · Cited by 5
Self-citation[Ulrasound diagnosis of melanoma metastasis into the gallbladder].
Vesnin, A. G., Семенов, И. И., Negustorov, I. F., Anisimov, V. V., & Turkevich, E. A. (1999) · PubMed · Cited by 5
Adaptive Brain-computer Interfacing through Error-related Potential Detection
Zeyl, T. J. (2016) · TSpace (University of Toronto) · Cited by 5
Lam, G., Rish, I., & Dixon, P. C. (2023) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2023.111606 · Cited by 4
Multimodal imaging technique for rapid response brain-computer interface feedback
Yeom, S.-K., Fazli, S., Mehnert, J., Blankcrtz, B., Steinbrink, J. M., Müller, K., & Lee, S. (2013) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2013.6506642 · Cited by 4
Self-citationMerrill, N., Maillart, T., Johnson, B., & Chuang, J. C.-I. (2015) · International Conference on Physiological Computing Systems (PhyCS) · doi:10.5220/0005238800440051 · Cited by 4
Modelling Non-stationarities in EEG Data with Robust Principal Component Analysis
Pascual, J., Kawanabe, M., & Vidaurre, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-21222-2_7 · Cited by 4
Self-citationAdaptive Classification Framework for Multiclass Motor Imagery-Based BCI
Nicolas-Alonso, L. F., Corralejo, R., Álvarez, D., & Hornero, R. (2013) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-319-00846-2_189 · Cited by 4
Explorative data analysis for changes in neural activity
Blythe, D. A. J., Meinecke, F. C., von Bünau, P., & Müller, K. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/2/026018 · Cited by 4
Self-citationTowards Electrocorticographic Electrodes for Chronic Use in BCI Applications
Henle, C., Schüettler, M., Rickert, J., & Stieglitz, T. (2012) · Biological and medical physics series · doi:10.1007/978-3-642-29746-5_5 · Cited by 4
Lin, Y., Jung, T., & Onton, Y. W. A. J. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch13 · Cited by 4
A Co-adaptive Training Paradigm for Motor Imagery Based Brain-Computer Interface
Xia, B., Zhang, Q., Xie, H., Li, S., Li, J., & He, L. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31346-2_49 · Cited by 3
Quantitative analysis of task selection for brain–computer interfaces
Llera, A., Gómez, V., & Kappen, H. J. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/056002 · Cited by 3
Alexis, B. C. O., Alejandra, D. L. K., Melissa, G. G. H., Nayeli, H. G., del Carmen, R. V. R., Mário, T., Paulina, O. C. L., del Carmen, T. C. M., & Braniff, D. L. T. V. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.08.004 · Cited by 3
Improving the Performance of Brain-Computer Interface Using Multi-modal Neuroimaging
Lee, M.-H., Fazli, S., Mehnert, J., & Lee, S. (2013) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2013.132 · Cited by 2
Decoding cognitive brain states
Porbadnigk, A. K., Treder, M. S., Fazli, S., Tangermann, M., Vidaurre, C., Haufe, S., Curio, G., Blankertz, B., & Miiller, K. R. (2013) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2013.6506613 · Cited by 2
Self-citationHigher order stationary subspace analysis
Panknin, D., von Bünau, P., Kawanabe, M., Meinecke, F. C., & Müller, K. (2016) · Journal of Physics Conference Series · doi:10.1088/1742-6596/699/1/012021 · Cited by 2
Self-citationDesign and Implementation of a Novel Subject-Specific Neurofeedback Evaluation and Treatment System
Issachar, G., Bar‐Shalita, T., Baruch, Y., Horing, B., & Portnoy, S. (2019) · Annals of Biomedical Engineering · doi:10.1007/s10439-019-02228-x · Cited by 2
Ortega, P., Colas, C., & Faisal, A. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/256701 · Cited by 2
Comparison of Preprocessing Algorithms using an Affordable EEG Headset
Sadiq, J., & Ra'ad, A. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017912949 · Cited by 1
Meta-Learning for BCI: A Promising New Direction
Barkley, C., Jain, R., Liang, S., & Wang, Y. (2025) · International conference on Fuzzy Theory and Its Applications · doi:10.1109/ifuzzy67152.2025.11287667 · Cited by 1
Towards Restoration and Rehabilitation of Motor Functions with the Help of Brain-Computer Interfaces
Muller-Putz, G. R., Solis‐Escalante, T., Wagner, J., Faller, J., Kaiser, V., Ofner, P., & Scherer, R. (2013) · Biosystems & biorobotics · doi:10.1007/978-3-642-34546-3_211 · Cited by 1
Brain-Machine Interfaces: From Restoring Sensorimotor Control to Augmenting Cognition
Moxon, K. A., Kong, Z., & Ditterich, J. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_36 · Cited by 1
Convolutional neural networks decode finger movements in motor sequence learning from MEG data
Zabolotniy, A., Chan, R. W., Moiseeva, V., & Fedele, T. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1623380 · Cited by 1
Abdennour, N., Margolles, P., & Soto, D. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09768-3 · Cited by 2
Towards robust machine learning methods for the analysis of brain data
Müller, K. R. (2018) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2018.8311495 · Cited by 1
Self-citationMerging Technology with Biology
Jung, R. (2011) · Wiley eBooks · doi:10.1002/9783527639366.ch1 · Cited by 1
Alonso, N., & Fernando, L. (2012) · UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · Cited by 1
Zhang, X. (2019) · Summit (Simon Fraser University) · Cited by 1
Tutorial on multimodal neuroimaging for brain-computer interfacing
Fazli, S., Müller, K., Lee, S., & Blankertz, B. (2013) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2013.6506605
Self-citationP374: Detecting spatial auditory attention in cocktail-party situations
Tangermann, M., Mueller, K., Nolte, A., Schumacher, J., Zhutovsky, P., & Blankertz, B. (2014) · Clinical Neurophysiology · doi:10.1016/s1388-2457(14)50483-x
Self-citationThe Design of an Intelligent EEG Monitoring and Control System
Wang, P., Xu, G., Wang, L., & Liu, C. L. (2014) · Applied Mechanics and Materials · doi:10.4028/www.scientific.net/amm.598.583
Multifrequency Analysis of Brain-Computer Interfaces
Fazli, S., Suk, H., Lee, S., & Müller, K. (2015) · Trends in augmentation of human performance · doi:10.1007/978-94-017-7239-6_4
Self-citationAdvances in Neurotechnology for Brain Computer Interfaces
Fazli, S. (2011) · DepositOnce · doi:10.14279/depositonce-3053
Investigation of EEG Correlate in NIRS Signal for BCI
Johar, A. H., Yousaf, T., Asgher, U., Ayaz, Y., Nazir, S., Khan, M. J., Mustafa, L., & Khalil, K. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-51041-1_42
Towards Context-aware Brain-computer Interfaces
Myrden, A. (2016) · TSpace (University of Toronto)
Brain-Machine Interfaces: From Restoring Sensorimotor Control to Augmenting Cognition
Moxon, K. A., Kong, Z., & Ditterich, J. (2022) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_36-1
Active Feedback using Riemannian Features for Motor Imagery Classification
Gupta, V., Mittal, R., Ameta, D., Behera, L., & Sandhan, T. (2023) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii58188.2023.10181030
EEG Signal Analysis: Prediction of Motor Actions
Chaudhari, A. K., Talekar, P., Shinde, G., & Thigale, A. (2023) · International Conference on Integration of Computational Intelligent System (ICICIS) · doi:10.1109/icicis56802.2023.10430260
EEG signal motion recognition based on GSA-SVM
Liu, Z., Chang, Y., Zhou, H., Zhang, H., Feng, Y., Zhang, W., Zhang, Z., & Liu, M. (2024) · International Conference on Machine Learning and Computer Application (ICMLCA) · doi:10.1117/12.3029160
Shrama, S. J., & Gupta, R. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-22158-3.00013-2
Emergencies and caregiver communication using brain-computer interfacing framework
Francis, S. V., Sheeba, G. M., Malathy, R., Mahalakshmi, B., Priyadharshini, P., & Priyanka, M. A. (2025) · AIP conference proceedings · doi:10.1063/5.0275743
Integrating EEG-SVM Confidence and RT via cBCI Enhances Team Decisions in a VR Drone TaskPreprint
Baker, C., Hinton, S., Nijjar, A., Poli, R., Cinel, C., Reed, T., & Fairclough, S. H. (2025) · Research Square · doi:10.21203/rs.3.rs-6985673/v1
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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
