Stability study of the optimal channel selection for emotion classification from EEG

In recent years, emotion classification from electroencephalogram (EEG) has attracted more and more attention. Because fewer channels were required in the real-time emotion classification system, many channel selection methods were proposed. In this paper, we investigated the stability of the optimal channels selected by channel selection algorithm, Mean-ReliefF-channel-selection (MRCS). EEG signals from 4 male students were recorded using 64 electrodes. With the comparisons of three effective channel sets obtained from the features (Shannon entropy (SE), differential entropy (DE) and 1st difference (1ST)) respectively, we explored the similarity of the first 10 optimal channels from the different sets. The experimental results indicated that there were average 75% common channels for the individual subject. But between subjects, the mean value of all combinations of subjects for different features can only reach 16%. This phenomenon indicates that the selected optimal channels from fewer effective features are also suitable for many other valid features. But the strong instability of channels selected by MRCS between subjects makes it a big challenge to guide an effective design of real-time emotion classification system.