TY - GEN
T1 - Adaptive EEG signal classification using stochastic approximation methods
AU - Sun, Shiliang
AU - Lan, Man
AU - Lu, Yue
PY - 2008
Y1 - 2008
N2 - Classification of time-varying electrophysiological signals is an important problem in the development of brain-computer interfaces (BCIs). Designing adaptive classifiers is a potential way to address this task. In this paper, Bayesian classifiers with Gaussian mixture models (GMMs) are adopted as the decision rule to classify electroencephalogram (EEG) signals. The stochastic approximation method (SAM) is used as the specific gradient descent method for updating the parameters of mean values and covariance matrices in the distribution of GMMs, where the parameters are simultaneously updated in a batch mode. Experimental results using data from a BCI show that the stochastic approximation method is effective for EEG classification tasks.
AB - Classification of time-varying electrophysiological signals is an important problem in the development of brain-computer interfaces (BCIs). Designing adaptive classifiers is a potential way to address this task. In this paper, Bayesian classifiers with Gaussian mixture models (GMMs) are adopted as the decision rule to classify electroencephalogram (EEG) signals. The stochastic approximation method (SAM) is used as the specific gradient descent method for updating the parameters of mean values and covariance matrices in the distribution of GMMs, where the parameters are simultaneously updated in a batch mode. Experimental results using data from a BCI show that the stochastic approximation method is effective for EEG classification tasks.
KW - Bayesian classifier
KW - Brain-computer interface (BCI)
KW - EEG signal classification
KW - Gaussian mixture model (GMM)
KW - Stochastic approximation method (SAM)
UR - https://www.scopus.com/pages/publications/51449086177
U2 - 10.1109/ICASSP.2008.4517634
DO - 10.1109/ICASSP.2008.4517634
M3 - 会议稿件
AN - SCOPUS:51449086177
SN - 1424414849
SN - 9781424414840
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 413
EP - 416
BT - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
T2 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Y2 - 31 March 2008 through 4 April 2008
ER -