Empirical mode decomposition: a method for analyzing neural data

Abstract

Almost all processes that are quantified in neurobiology are stochastic and nonstationary. Conventional methods that characterize these processes to provide a meaningful and precise description of complex neurobiological phenomenon may be insufficient. Here, we report on the use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of macaque monkeys performing a visual spatial attention task. We found that local field potentials were resolved by the EMD into the sum of a set of intrinsic components with different degrees of oscillatory content. High-frequency components were identified as gamma band (35–90 Hz) oscillations, whereas low-frequency components in single-trial recordings contributed to the average visual evoked potential (AVEP). Comparison with Fourier analysis showed that EMD may offer better temporal and frequency resolution. The EMD, coupled with instantaneous frequency analysis, may prove to be a vital technique for the analysis of neural data.