运动表象
脑-机接口
计算机科学
模式识别(心理学)
人工智能
线性判别分析
稀疏逼近
方案(数学)
代表(政治)
接口(物质)
空间滤波器
上下文图像分类
脑电图
图像(数学)
数学
精神科
心理学
最大气泡压力法
数学分析
政治
气泡
并行计算
法学
政治学
作者
Younghak Shin,Seungchan Lee,Jun-Ho Lee,Heung-No Lee
出处
期刊:Journal of Neural Engineering
[IOP Publishing]
日期:2012-08-07
卷期号:9 (5): 056002-056002
被引量:87
标识
DOI:10.1088/1741-2560/9/5/056002
摘要
Motor imagery (MI)-based brain-computer interface systems (BCIs) normally use a powerful spatial filtering and classification method to maximize their performance. The common spatial pattern (CSP) algorithm is a widely used spatial filtering method for MI-based BCIs. In this work, we propose a new sparse representation-based classification (SRC) scheme for MI-based BCI applications. Sensorimotor rhythms are extracted from electroencephalograms and used for classification. The proposed SRC method utilizes the frequency band power and CSP algorithm to extract features for classification. We analyzed the performance of the new method using experimental datasets. The results showed that the SRC scheme provides highly accurate classification results, which were better than those obtained using the well-known linear discriminant analysis classification method. The enhancement of the proposed method in terms of the classification accuracy was verified using cross-validation and a statistical paired t-test (p < 0.001).
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