Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusi...Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.展开更多
文摘Due to the volume conduction,electroencephalogram（EEG） gives a rather blurred image of brain activities. It is a challenge for generating satisfactory performance with EEG. This paper studies the multiple areas fusion of EEG classifiers to improve the motor imagery EEG classification performance. Two feature extraction methods are employed to extract the feature from three different areas of EEG. One is power spectral density（PSD）, and the other is common spatial patterns（CSP）. Classifiers are designed based on the well-known linear discrimination analysis（LDA）. The fusion of the individual classifiers is realized by means of the Choquet fuzzy integral. It is demonstrated that the proposed method comes with better performance compared with the individual classifier.
