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RESEARCH PRODUCT

Empirical Mode Decomposition on Mismatch Negativity

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description

Empirical mode decomposition (EMD) has been applied in the various disciplines to extract the desired signal. The basic principle is to decompose a time series into intrinsic mode functions (IFMs) and each IFM corresponds to an oscillation phenomenon. A statistical description of the oscillatory activities of the EEG has been well known. It is desired to extract single oscillatory process from the EEG by EMD. Mismatch negativity (MMN) can be automatically elicited by the deviant stimulus in an oddball paradigm, in which physically the deviant stimulus occurs among repetitive and homogeneous stimuli. MMN thus reflects the ability of the brain to detect changes in auditory stimuli. So, the MMN trace indeed is the superposition of different event related potentials (ERPs). In theory, different ERPs correspond to different oscillatory phenomena. We assume each oscillatory activity corresponds to an IMF. Hence, EMD can decompose the MMN trace into different IMFs. Based on the timing, spectral and time-frequency infomax of MMN, a MMN detector is also designed to choose the MMN-like IMF. In contrast to the classic methods—averaging and optimal digital filtering (ODF), the EMD can cancel the ERPs overlapped with MMN both in the time and frequency domain. In the computation on the MMN experiment dataset, EMD outperforms averaging and ODF with about 3dB higher support to absence ratio.