Analysis of neural activity from EEG data based on EMD frequency bands

Empirical Mode Decomposition (EMD) is an emerging tool in signal analysis, specifically in systems with Nonlinear and/or nonstationary properties such as EEG signals. Its use is motivated by the fact that EMD can give an effective and meaningful time-frequency information about the signal. The EMD decomposes the signal in intrinsic mode functions (IMF) that represent the signal in different frequency bands. In this paper, we propose a novel method for feature extraction of EEG signals based on multi-band brain mapping using EMD, where the EMD is applied to decompose an EEG signal into a set of intrinsic mode functions (IMF). The obtained multi-band brain mapping is used to reconstruct the neuronal activity of the brain. The impact of signal to noise ratio (SNR) on the EMD frequency bands separation is explored and the results shows the strength of the EMD in adaptively separating the noise. The neural reconstruction obtained based on EMD is compared to the case in which EMD pre-processing is not employed, verifying the effective noise separation with EMD.