A novel method for reliable and fast extraction of neuronal EEG/MEG oscillations on the basis of spatio-spectral decomposition

Neuronal oscillations have been shown to underlie various cognitive, perceptual and motor functions in the brain. However, studying these oscillations is notoriously difficult with EEG/MEG recordings due to a massive overlap of activity from multiple sources and also due to the strong background noise. In EEG/MEG research frequently used algorithms are based on independent component analysis (ICA) not requiring inverse modeling calculations. These ICA approaches might meet intrinsic limitations for regular EEG/MEG oscillatory components which not only represent a mixture of narrow-band signals with very similar spectra but are also fairly Gaussian distributed. Moreover, the majority of ICA methods are based on numerical approaches, which do not always guarantee adequately converging solutions, and, in addition, computing ICA can be time consuming. Here we present a novel method for the reliable and fast extraction of neuronal oscillations from multi-channel EEG/MEG/LFP recordings. The method is based on a linear decomposition of recordings: it maximizes the signal power at a peak frequency while simultaneously minimizing it at the neighboring, surrounding frequency bins. Such procedure leads to the optimization of signal-to-noise ratio and allows extraction of components with a characteristic „peaky“ spectral profile, which is typical for oscillatory processes. We refer to this method as spatio-spectral decomposition (SSD). Our simulations demonstrate that the method allows a reliable extraction of oscillatory signals even with a signal-to-noise ratio as low as 1:10. The SSD also outperformed conventional approaches based on ICA. Using real EEG data we also show that SSD allows extraction of neuronal oscillations (e.g., in α frequency range) with highest signal-to-noise ratio and with the spatial patterns corresponding to central and occipito-parietal sources. Importantly, running time for SSD is only a few milliseconds, which clearly distinguishes it from other extraction techniques usually requiring seconds of computational time. Due to the high accuracy and speed, we suggest that SSD can be used as a reliable method for the extraction of neuronal oscillations from multi-channel electrophysiological recordings.