基于P300电位的脑机接口系统中参数优化问题的研究

The Study of Parameter Optimization in P300-based Brain Computer Interface

基于P300电位的脑机接口是脑机接口系统研究中的重要范式之一。本研究探讨双极导联选择、滤波器设计和特征窗长度这三者中,参数选择对分类正确率的影响。使用的数据为6名受试者进行12选择Oddball范式的实验数据,数据分类采用支持向量机。结果表明,通过导联优选,对不同的受试者进行定制,可以有效提高分类正确率。滤波器频带对分类正确率有一定影响,但是滤波器阶次对结果影响不大。随着特征窗长度的增加,分类正确率也会提高,因此使用较长特征窗有助于提高分类正确率。本研究结果推荐的数据处理过程为：通过导联优选为每位受试者定制最优的双极导联;滤波过程使用0.5-16 Hz,阶次为20的FIR滤波器,然后把数据降采样为32Hz;特征窗提取时可选择刺激后0-600 ms的数据。

The P300 based brain computer interface （BCI）is one of the important models in BCI study. This paper studied the parameter optimization in bipolar lead selection, filter design and temporal window length selection. The data came from 6 subjects in an oddball paradigm with 12 choices. Support vector machine was adopted for classification. The results illustrated that the classification accuracy could be enhanced by bipolar lead optimization for individuals. The pass band of the filter influenced on the classification accuracy, but the order of filter did not. Also, the classification accuracy could benefit from a long temporal window. With the above results, a procedure of parameter optimization was proposed. Bipolar lead was acquired for individuals during the data preprocessing at first. The data could be down-sampled to 32 Hz after a 0.5 - 60 Hz band pass FIR filter with an order of 20. A fixed temporal window of 0 - 600 ms after stimuli was a good choice for feature extraction.