基于共空间模式和功率谱密度的脑电信号分类

由于单一域缺少其他相关信息而导致运动想象分类准确率不高和泛用性差，本文研究设计了基于空间域和频域的运动想象分类方法。根据运动想象执行时的对侧映射机制以及事件相关同步和事件相关去同步的现象，对C3和C4通道数据进行共空间模式和功率谱密度特征提取和融合，然后使用网格搜索参数优化的支持向量机对运动想象的脑电信号进行分类。结果表明共空间模式和功率谱密度的融合特征，解决了共空间模式对噪声敏感以及缺少频率特征信息的缺点，实现了更高的分类结果和泛化性，分类准确率达91.3%,验证了该方法的有效性。

Classification of EEG Signals Based on Common Space Pattern and Power Spectrum Density

In view of this problem of low accuracy and poor universality due to lack of other relevant information, a motor imagination classification method based on spatial and frequency domain features was proposed. According to event related synchronization, event related desynchronization, and the contralateral mapping mechanism during moving, the combined features of right and left hands extracted by common spatial pattern and power spectrum density from the channels of C3 and C4 were classified using support vector machine. The results show that the combining features solved the problem of common spatial pattern is sensitive to noise, and made up for the lack of frequency features information. A higher classification result and generalization was also achieved, the classification accuracy was up to 91.3%, which verified the effectiveness of the method proposed in this paper.