Mental tasks classification and their EEG structures analysis by using the growing hierarchical self-organizing map

Abstract

The unsupervised method of growing hierarchical self-organizing map (GHSOM) was used to perform mental tasks classification. The GHSOM is an adaptive artificial neural network model with hierarchical architecture that is able to detect the hierarchical structure of data. The results indicate that GHSOM provides more detailed clustering information than SOM, and gives visual information about the separability of mental tasks in an intuitive way. The average classification accuracy across 130 task pairs by using GHSOM was up to 96.7%.