Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals

Demanuele, C., James, C.J. and Sonuga-Barke, E.J.S. (2007) Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals. Behavioral and Brain Functions, 3 (62). (doi:10.1186/1744-9081-3-62).

Abstract

Background:
It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) - below 0.5 Hz - which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc.

Methods:
In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral whitening. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis.

Results:
Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and evoked response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.

Conclusions:
Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

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