Abstract
This paper attempts to explain some methodological issues regarding EEG signal analysis which might lead to misinterpretation and therefore to unsubstantiated conclusions. The so called “split-alpha,” a “new phenomenon” in EEG spectral analysis described lately in few papers is such a case. We have shown that spectrum feature presented as a “split alpha” can be the result of applying improper means of analysis of the spectrum of the EEG signal that did not take into account the significant properties of the applied Fast Fourier Transform (FFT) method. Analysis of the shortcomings of the FFT method applied to EEG signal such as limited duration of analyzed signal, dependence of frequency resolution on time window duration, influence of window duration and shape, overlapping and spectral leakage was performed. Our analyses of EEG data as well as simulations indicate that double alpha spectra called as “split alpha” can appear, as spurious peaks, for short signal window when the EEG signal being studied shows multiple frequencies and frequency bands. These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage. Our paper is intended to explain the reasons underlying a spectrum pattern called as a “split alpha” and give some practical indications for using spectral analysis of EEG signal that might be useful for readers and allow to avoid EEG spectrum misinterpretation in further studies and publications as well as in clinical practice.
Highlights
Few papers have been published describing a “new phenomenon” in EEG signal spectral analysis called “split alpha.” What the respective authors mean by this is “the presence of two or more peaks with close frequencies in the EEG frequency alpha band” (Olejarczyk et al, 2017)
A power spectrum of a signal which contains a band of frequencies results in the emergence of peaks in the spectrum at the edges of the frequency band
These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage
Summary
Few papers have been published describing a “new phenomenon” in EEG signal spectral analysis called “split alpha.” What the respective authors mean by this is “the presence of two or more peaks with close frequencies in the EEG frequency alpha band” (Olejarczyk et al, 2017). What the respective authors mean by this is “the presence of two or more peaks with close frequencies in the EEG frequency alpha band” (Olejarczyk et al, 2017). In order to achieve that, the authors have analyzed EEG recordings “using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity.”. They tried to “test the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called ‘split alpha.”’ This term has been coined by Robinson et al (2003) in their modeling calculations using a model of corticothalamic system when incorporating parameter non-uniformities into his previously developed uniform model (Robinson et al, 2001, 2003) and developed in further studies of Robinson’s group (Robinson et al, 2001, 2003; O’Connor and Robinson, 2004; Xiong and Yao, 2005; Gray and Robinson, 2013). Chiang et al (2008, 2011) developed a method for the automatic identification of multiple alpha peaks in EEG data.
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