EMD approach to multichannel EEG data analysis—The amplitude and phase components clustering analysis

Human brains exhibit a possibility to control directly the intelligent computing applications in form of brain computer/machine interfacing (BCI/BMI) technologies. Neurophysiological sig-nals and especially electroencephalogram (EEG) are the forms of brain electrical activity which can be easily captured and utilized for BCI/BMI applications. Those signals are unfortunately usually very highly contaminated by external noise caused by the presence of di®erent devices in the environment creating electromagnetic interference. In this paper, we ¯rst decompose each of the recorded channels, in multichannel EEG recording environment, into intrinsic mode func-tions (IMF) which are a result of empirical mode decomposition (EMD) extended to multi-channel analysis. We present novel and interesting results on human mental and cognitive states estimation based on analysis of the above-mentioned stimuli-related IMF components. The IMF components are further clustered for their spectral similarity in order to identify only those carrying responses to present stimuli to the subjects. The resulting targets onl