Growing Hierarchical SOM Models for Mental Tasks Classification
应用生长、分级的SOM模型进行意识任务分类

The growing hierarchical self-organizing map(GHSOM)model was proposed to apply to performing mental tasks classification in EEG for Brain-Computer Interface.The GHSOM model is a variant of SOM,and consists of many layers of SOMs,which form the hierarchical architecture.The hierarchical structure hid in data can be expressed by GHSOM model.The effectiveness of GHSOM models implemented using both the mean quantization error(mqe)and quantization error(qe)methods for mental tasks classification was investigated.The results indicated that GHSOM models provided visual information about the separability of different mental tasks,and the GHSOM model using quantization error method provided more detailed information about data and obtained high classification accuracy.About 80% of the average classification accuracy for five mental tasks classifications was achieved by using the GHSOM model.