Abstract

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested 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.” Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Highlights

  • There has been increased interest in understanding the mechanisms of generation of one of the basic patterns of electroencephalographic (EEG) activity—alpha waves

  • Several patients illustrate the impact of choice of reference electrode, in addition to window size and volume conduction

  • We found that the split alpha peak was a common phenomenon observed in EEG data

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Summary

Introduction

There has been increased interest in understanding the mechanisms of generation of one of the basic patterns of electroencephalographic (EEG) activity—alpha waves. An individual alpha frequency depends on many factors, including age, gender, level of sleepiness, or presence of neurological disorder. All these factors can have an impact on the separation of theta and alpha bands (Klimesch, 1999; Garn et al, 2012; Grandy et al, 2013a; Bazanova and Vernon, 2014). Change in spectral characteristics of EEG patterns has been observed in various neurological disorders (Garces et al, 2013; Ponomareva et al, 2013; Zappasodi et al, 2014; Vollebregt et al, 2015)

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