The Offline Feature Extraction of Four-class Motor Imagery EEG Based on ICA and Wavelet-CSP

被引：0
|
作者：
Bai Xiaoping [1 ]
Wang Xiangzhou [1 ]
Zheng Shuhua [1 ]
Yu Mingxin [1 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
来源：
2014 33RD CHINESE CONTROL CONFERENCE (CCC) | 2014年
关键词：
Brain-computer-interface (BCI); electroencephalogram (EEG); ICA; Wavelet-CSP; SVM; SINGLE-TRIAL EEG; CLASSIFICATION; AREA;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The signal processing of electroencephalogram (EEG) is the key technology in a brain-computer interface (BCI) system. A widely used method is to purify the raw EEG with an 8-30Hz band-pass filter and extract features by common spatial patterns (CSP). However its results for BCI Competition IV are not very satisfactory. To improve the classification success rate, this paper proposed a novel Wavelet-CSP with ICA-filter method. For the data sets from BCI Competition IV, the features of the four-class motor imagery were trained and tested using the Support Vector Machines (SVM). The experimental results showed that the proposed method had a higher average kappa coefficient of 0.68 than 0.52 of the general method.
引用
收藏
页码：7189 / 7194
页数：6
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