基于改进GHSOM的运动想象脑电信号自适应识别方法

为解决运动想象脑电信号(MI-EEG)的识别方法泛化能力受限和自适应性差等问题,对传统的生长、分层自组织映射神经网络(GHSOM)进行改进,并提出一种主成分分析法(PCA)与改进的GHSOM神经网络(IGHSOM)相结合的脑电自适应识别方法。由于IGHSOM能够根据上一层扩展神经元的量化误差进行自动分层判断,使得其不仅对数据映射更加准确和详细,而且增强了网络的稳定性和自适应性。基于脑机接口(BCI)竞赛数据库,利用PCA进行特征提取,以IGHSOM为分类器进行实验研究。结果表明,该方法获得了较高的识别精度,验证了GHSOM改进策略及该识别方法的正确性和有效性。

Adaptive recognition method based on improved GHSOM for motor imagery EEG

To solve the limited generalization and poor adaptability of the recognition method for motor imagery electroencephalography (MI EEG), the traditional growing hierarchical self organizing map (GHSOM) neural network is improved, and an adaptive recognition method is proposed based on principal component analysis (PCA) and improved GHSOM (IGHSOM) neural network. The hierarchy growth judgment is automatically accomplished according to the quantization error of the expansion neurons in upper layer. Thus, IGHSOM can not only reflect the mapping data more accurately and in more details, but also improve the stability and adaptive ability of the network. The experiment on the BCI competition data set was conducted to assess the recognition method; the PCA was used to extract the MI EEG features, and IGHSOM was employed to classify the features. The experiment results indicate that the proposed method achieves high recognition accuracy, which verifies the correction and effectiveness of the improved strategy of GHSOM and the proposed recognition approach.