Analyzing the EEG energy of healthy human, comatose patient and brain death using multivariate empirical mode decomposition algorithm

Analysis of EEG energy is a useful technique in the brain signal processing especially in the determination of brain death. In this paper, we attempt to analyze EEG energy of three different conscious states such as normal awake, comatose and brain death based on multivariate empirical mode decomposition (MEMD) algorithm. The MEMD is a fully data-driven time-frequency technique which adaptively decomposes a set of signals into a finite set of amplitude-frequency modulated components, namely intrinsic mode functions (IMFs). By selecting suitable IMFs, we can calculate and evaluate the EEG energy of the decomposed brain activities. The analyzed results illustrate the effectiveness and performance of the proposed method in calculation of EEG energy for the healthy human, comatose patient and brain death.