A new algorithm for multi-channel EEG signal analysis using mutual information
Electroencephalogram (EEG) signals have long been used for the analysis of brain activities and for the detection of abnormalities (such as seizures). More recently, and with advance of computer technology, we have seen new applications using EEG signals in the control of PC keyboards through BCIs (Brain Computer Interfaces). These EEG signals are normally collected through multi-sensors (8,12, or 16 channels). For proper interpretation of such data, several techniques have been proposed to extract features from the collected multi-channel data, then analyse them, or classify them into patterns. However, most existing techniques do not take into consideration the inherent relationship among features across channels. Here, we propose a scheme based on a hybrid information maximization concept (HIM) to process multi-channel data for optimal feature extraction. The experiments carried show a clear advantage of the approach over principal component and canonical correlation analysis.