On association study of scalp EEG data channels under different circumstances

Abstract

Electroencephalography (EEG) is an electrophysiological monitoring method to record electrical activity of the brain using different electrodes, which are considered as the EEG channels that are placed on scalp. In this paper, we propose an effective information processing approach to explore the association among EEG channels under different circumstances. Particularly, we design four different experimental scenarios and record the EEG signals under motions of eye-opening and body-movement. With sequences of data collected in time order, we first compute the mutual conditional entropy to measure the association between two electrodes. Using the hierarchical clustering tree and data mechanics algorithm, we could effectively identify the association between particular EEG channels under certain motion scenarios. We also implement the weighted random forest to further classify the classes (experimental scenarios) of the EEG time series. Our evaluation results show that we could successfully classify the particular motions with given EEG data series.