Joint Channel-frequency Selection for Motor Imagery-based BCIs Using a Semi-supervised SVM Algorithm

被引：0
|
作者：
Li Yuanqing [1 ]
Long Jinyi [1 ]
机构：
[1] South China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China
来源：
PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE | 2010年
基金：
中国国家自然科学基金;
关键词：
Electroencephalogram (EEG); Motor Imagery; Brain Computer Interfaces (BCIs); Channel; Frequency Band; Semi-supervised Learning; SINGLE-TRIAL EEG; CLASSIFICATION; FILTERS;
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：
In this paper, motor imagery-based Brain computer interfaces (BCIs) are considered, in which channels and frequency band are two important parameters. A semi-supervised support vector machine algorithm is proposed for joint channel-frequency selection automatically and adaptively. This algorithm is designed for small training data case, in which the training data set is insufficient for parameter setting. Our algorithm is then applied to a BCI competition data set. Data analysis results are presented and the effectiveness of this algorithm is demonstrated.
引用
收藏
页码：2949 / 2952
页数：4
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