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Neuroimage. 2017 Feb 15;147:43-56. doi: 10.1016/j.neuroimage.2016.11.036. Epub 2016 Dec 1.

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation.

Author information

1
Radboud University and Radboud University Medical Center, Donders Center for Neuroscience, Netherlands. Electronic address: mikexcohen@gmail.com.
2
Centre de Recherche Cerveau & Cognition, Toulouse, France.

Abstract

Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results.

KEYWORDS:

Components analysis; Data analysis; Denoising source separation; Generalized eigendecomposition; Neural oscillations; SSVEP; Spatiotemporal filtering

[Indexed for MEDLINE]

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