BCI meeting 2005-workshop on BCI signal processing: feature extraction and translation
Abstract
This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.
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?
Each BCI paradigm relies on manual specification of signal processing, feature extraction and classification methods, requiring significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.
“each paradigm relies on manual specification of signal processing [7], feature extraction [8] and classification methods [9], a process which often requires significant subject-matter expertise and/or a priori knowledge about the expected EEG signal.”
From EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces (2018) · cites this paper as [8]
Cites
Motor imagery classification by means of source analysis for brain–computer interface applications
Qin, L., Ding, L., & He, B. (2004) · Journal of Neural Engineering · doi:10.1088/1741-2560/1/3/002 · Cited by 239 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces
Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aace8c · Cited by 5,115 · 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
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
Xu, R., Jiang, N., Lin, C., Mrachacz-Kersting, N., Dremstrup, K., & Farina, D. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2013.2294203 · Cited by 219 · see citing works
Emulation of computer mouse control with a noninvasive brain–computer interface
McFarland, D. J., Krusienski, D. J., Sarnacki, W. A., & Wolpaw, J. R. (2008) · Journal of Neural Engineering · doi:10.1088/1741-2560/5/2/001 · Cited by 202 · see citing works
Self-citationA Collaborative Brain-Computer Interface for Improving Human Performance
Wang, Y., & Jung, T.-P. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020422 · Cited by 147 · see citing works
Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)
Witkowski, M., Cortese, M., Cempini, M., Mellinger, J., Vitiello, N., & Soekadar, S. R. (2014) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-11-165 · Cited by 83 · see citing works
- Authors
- McFarland, D. J., Anderson, C. W., Muller, K.-R., Schlogl, A., & Krusienski, D. J.
- Affiliations
- New York State Department of Health (US)
- Colorado State University (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2006-06
- Volume
- 14
- Issue
- 2
- Pages
- 135-138
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2133988362
- Cited by
- 204
- References
- 1
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 6 · 2023: 10 · 2022: 3 · 2021: 11 · 2020: 6 · 2019: 13 · 2018: 10 · 2017: 10 · 2016: 8 · 2015: 14 · 2014: 13 · 2013: 9 · 2012: 15
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- National Institutes of Health (1r13eb005114-01)
- National Institute of Biomedical Imaging and Bioengineering (R13EB00511401)
- 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, feature extraction, feature translation
- MeSH terms
- Algorithms, Artificial Intelligence, Biotechnology, Brain, Communication Devices for People with Disabilities, Communication Aids for Disabled, Electroencephalography, Humans, Man-Machine Systems, Neuromuscular Diseases, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, User-Computer Interface, Internationality
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
