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BCI2000: A General-Purpose Brain-Computer Interface (BCI) System
Abstract
Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.
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Self-citationParametric Orchestral Sonification of EEG in Real Time
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Self-citationStocco, A., Prat, C. S., Losey, D. M., Cronin, J. A., Wu, J., Abernethy, J. A., & Rao, R. P. N. (2015) · PLOS ONE · doi:10.1371/journal.pone.0137303 · Cited by 57
“Signal processing and data storage were managed through the BCI2000 software package [17].”
MethodBenefits of deep learning classification of continuous noninvasive brain–computer interface control
Stieger, J. R., Engel, S. A., Suma, D., & He, B. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac0584 · Cited by 55
Capsule Network for ERP Detection in Brain-Computer Interface
Ma, R., Yu, T., Zhong, X., Yu, Z. L., Li, Y., & Gu, Z. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3070327 · Cited by 47
Oscillatory phase modulates the timing of neuronal activations and resulting behavior
Coon, W. G., Gunduz, A., Brunner, P., Ritaccio, A. L., Pesaran, B., & Schalk, G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.080 · Cited by 41
Self-citationWeaver, K. E., Wander, J. D., Ko, A. L., Casimo, K., Grabowski, T. J., Ojemann, J. G., & Darvas, F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.043 · Cited by 44
Asynchronous gaze-independent event-related potential-based brain–computer interface
Aloise, F., Aricò, P., Schettini, F., Salinari, S., Mattia, D., & Cincotti, F. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.07.006 · Cited by 43
P300 Speller Performance Predictor Based on RSVP Multi-feature
Won, K., Kwon, M., Jang, S., Ahn, M., & Jun, S. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00261 · Cited by 34
Choi, J., Kwon, M., & Jun, S. C. (2020) · Sensors · doi:10.3390/s20102770 · Cited by 35
BRAND: a platform for closed-loop experiments with deep network models
Ali, Y. H., Bodkin, K., Rigotti-Thompson, M., Patel, K., Card, N. S., Bhaduri, B., Nason-Tomaszewski, S. R., Mifsud, D. M., Hou, X., Nicolas, C., et al. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad3b3a · Cited by 32
Detecting Human Trust Calibration in Automation: A Convolutional Neural Network Approach
Choo, S., & Nam, C. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3137015 · Cited by 34
IENet: a robust convolutional neural network for EEG based brain-computer interfaces
Du, Y., & Liu, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7257 · Cited by 34
“The raw EEG data was digitized and low-pass filtered in the amplifier software VisionRecorder and sent to BCI2000 [89] for data storage.”
MethodPre-training in Medical Data: A Survey
Qiu, Y., Lin, F., Chen, W., & Xu, M. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1382-8 · Cited by 30
Subject adaptation convolutional neural network for EEG-based motor imagery classification
Liu, S., Zhang, J., Wang, A., Wu, H., Zhao, Q., & Long, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac9c94 · Cited by 29
P300 event-related potential detection using one-dimensional convolutional capsule networks
Liu, X., Xie, Q., Lv, J., Huang, H., & Wang, W. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114701 · Cited by 23
Weighted extreme learning machine for P300 detection with application to brain computer interface
Kong, W., Guo, S., Long, Y., Peng, Y., Zeng, H., Zhang, X., & Zhang, J. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0840-1 · Cited by 22
Angrick, M., Luo, S., Rabbani, Q., Candrea, D. N., Shah, S., Milsap, G. W., Anderson, W. S., Gordon, C. R., Rosenblatt, K. R., Clawson, L., et al. (2023) · doi:10.1101/2023.06.30.23291352 · Cited by 95
Handling Missing Sensors in Topology-Aware IoT Applications with Gated Graph Neural Network
Liu, S., Yao, S., Huang, Y., Liu, D., Shao, H., Zhao, Y., Li, J., Wang, T., Wang, R., Yang, C., et al. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411818 · Cited by 20
Decoding visual motions from EEG using attention based RNN.
Yang, D., Liu, Y., Zhou, Z., Yu, Y., & Liang, X. (2020) · Applied Sciences · doi:10.3390/app10165662 · Cited by 20
Afergan, D., Shibata, T., Hincks, S. W., Peck, E. M., Yuksel, B. F., Chang, R., & Jacob, R. J. K. (2014) · Proceedings of the 27th annual ACM symposium on User interface software and technology · doi:10.1145/2642918.2647414 · Cited by 19
Shuqfa, Z., Lakas, A., & Belkacem, A. N. (2024) · Data in Brief · doi:10.1016/j.dib.2024.110181 · Cited by 16
Fernández-Rodríguez, Á., Medina-Juliá, M. T., Velasco-Álvarez, F., & Ron-Angevin, R. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20521-8_31 · Cited by 5
Metadata
- Authors
- Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N., & Wolpaw, J. R.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2004-06
- Volume
- 51
- Issue
- 6
- Pages
- 1034-1043
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2151669316
- Cited by
- 3,024
- References
- 3
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.