Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface
Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface
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DOI：
10.1109/tnsre.2007.916303
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发表时间：
2008-04-01
影响因子：
4.9
通讯作者：
Zhou, Peng
中科院分区：
文献类型：
--
作者：
Besio, Walter G.;Cao, Hongbao;Zhou, Peng
对于严重残疾的人来说，脑机接口（BCI）可能是一种可行的通信手段。Lapalacian脑电图（EEG）已被证明可以改善EEG识别中的分类。在这项工作中，从三极同心电极和圆盘电极的信号的有效性进行了比较，用作脑机接口。采集两组左/右手运动想象EEG信号。提出了一种自回归（AR）模型进行特征提取，并采用基于马氏距离的线性分类器进行分类。在特征提取之前，采用穷举选择算法分析三个因素。分析的因素是1）每次试验中使用的数据长度，2）数据的开始位置，3）AR模型的顺序。结果表明，三极同心电极产生的分类精度显着高于圆盘电极。
For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.