The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Characterization of four-class motor imagery EEG data for the BCI-competition 2005
Open accessAbstract
To determine and compare the performance of different classifiers applied to four-class EEG data is the goal of this communication. The EEG data were recorded with 60 electrodes from five subjects performing four different motor-imagery tasks. The EEG signal was modeled by an adaptive autoregressive (AAR) process whose parameters were extracted by Kalman filtering. By these AAR parameters four classifiers were obtained, namely minimum distance analysis (MDA)--for single-channel analysis, and linear discriminant analysis (LDA), k-nearest-neighbor (kNN) classifiers as well as support vector machine (SVM) classifiers for multi-channel analysis. The performance of all four classifiers was quantified and evaluated by Cohen's kappa coefficient, an advantageous measure we introduced here to BCI research for the first time. The single-channel results gave rise to topographic maps that revealed the channels with the highest level of separability between classes for each subject. Our results of the multi-channel analysis indicate SVM as the most successful classifier, whereas kNN performed worst.
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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
Brain Computer Interfaces, a Review
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012) · Sensors · doi:10.3390/s120201211 · Cited by 2,143
Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms
Lotte, F., & Guan, C. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2082539 · Cited by 1,045
A novel deep learning approach for classification of EEG motor imagery signals
Tabar, Y. R., & Halici, U. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2560/14/1/016003 · Cited by 859
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Yger, F., Berar, M., & Lotte, F. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2627016 · Cited by 420
Metadata
- Authors
- Schlögl, A., Lee, F., Bischof, H., & Pfurtscheller, G.
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2005-08-15
- Volume
- 2
- Issue
- 4
- Pages
- L14-L22
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- OpenAlex
- W2057219090
- Cited by
- 414
- References
- 27
- FWCI
- 4.05
- Type
- journal article
- Language
- en
- Open access
- bronze
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