title: Simple adaptive sparse representation based classification schemes for EEG based brain-computer interface applications.
abstract: One of the main problems related to electroencephalogram (EEG) based brain-computer interface (BCI) systems is the non-stationarity of the underlying EEG signals. This results in the deterioration of the classification performance during experimental sessions. Therefore, adaptive classification techniques are required for EEG based BCI applications. In this paper, we propose simple adaptive sparse representation based classification (SRC) schemes. Supervised and unsupervised dictionary update techniques for new test data and a dictionary modification method by using the incoherence measure of the training data are investigated. The proposed methods are very simple and additional computation for the re-training of the classifier is not needed. The proposed adaptive SRC schemes are evaluated using two BCI experimental datasets. The proposed methods are assessed by comparing classification results with the conventional SRC and other adaptive classification methods. On the basis of the results, we find that the proposed adaptive schemes show relatively improved classification accuracy as compared to conventional methods without requiring additional computation.
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
authors.forename: Younghak
authors.initials: Y
authors.lastname: Shin
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
authors.forename: Seungchan
authors.initials: S
authors.lastname: Lee
authors.affiliation: Department of Neuroscience, Brown University, RI, USA.
authors.forename: Minkyu
authors.initials: M
authors.lastname: Ahn
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
authors.forename: Hohyun
authors.initials: H
authors.lastname: Cho
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
authors.forename: Sung Chan
authors.initials: SC
authors.lastname: Jun
authors.affiliation: School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea. Electronic address: heungno@gist.ac.kr.
authors.forename: Heung-No
authors.initials: HN
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001921:Brain; D062207:Brain-Computer Interfaces; D016208:Databases, Factual; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D019018:Imagery, Psychotherapy; D016014:Linear Models; D009048:Motor Skills; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface
keywords: Brain–computer interface (BCI); Common spatial pattern (CSP); Electroencephalogram (EEG); L1 minimization; Non-stationarity; Sparse representation based classification (SRC)