Multi-class imagery EEG recognition based on adaptive subject-based feature extraction and SVM-BP classifier - Details

Author：

Li, Mingai (Li, Mingai.) (Scholars：李明爱) | Lin, Lin (Lin, Lin.) | Jia, Songmin (Jia, Songmin.) (Scholars：贾松敏)

Indexed by：

EI Scopus

Abstract：

In　brain-computer　interface　(BCI),　the　classification　accuracy　significantly　drops　when　the　system　has　multiple　motor　imagery　tasks　for　different　subjects.　To　improve　the　classification　accuracy　and　individual　adaptability　of　the　system,　a　new　method　of　feature　extraction　and　classification　is　presented　in　this　paper　for　recognizing　the　four　different　motor　imagery　tasks　(right　hand,　left　hand,　foots　and　tongue　movements).　Wavelet　packet　basis　is　selected　for　extracting　the　frequency　bands　in　which　the　feature　can　be　classified　easily.　Then,　the　EEG　feature　is　extracted　from　the　frequency　bands　information　with　One　Versus　the　Rest　Common　Spatial　Patterns　(OVR-CSP)　algorithm.　Furthermore,　a　hybrid　classification　model　of　Support　Vector　Machines　combining　with　the　Back　Propagation　neural　network　(SVM-BP)　is　built　to　classify　the　multi-class　EEG　feature.　Experiment　results　show　that　the　proposed　approach　achieves　better　performance　than　other　methods　and　it　has　adaptability　for　different　subjects　to　some　extent.　©　2011　IEEE.

Keyword：

Brain computer interface Biomedical signal processing Classification (of information) Support vector machines Image enhancement Feature extraction Image classification Extraction Backpropagation Electroencephalography Neural networks

Author Community：

[ 1 ] [Li, Mingai]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Lin, Lin]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Jia, Songmin]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China