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Outlier detection for single-trial EEG signal analysis

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5 Author(s)
Boyu Wang ; Department of Electrical and Electronics Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau ; Feng Wan ; Peng Un Mak ; Pui In Mak
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The performance of a brain computer interface (BCI) system is usually degraded due to the outliers in electroencephalography (EEG) samples. This paper presents a novel outlier detection method based on robust learning of Gaussian mixture models (GMMs). We apply the proposed method to the single-trial EEG classification task. After trial-pruning, feature extraction and classification are performed on the subset of training data, and experimental results demonstrate that the proposed method can successfully detect the outliers and therefore achieve more reliable result.

Published in:

Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on

Date of Conference:

April 27 2011-May 1 2011