Some methods of automatic measurement of the electroencephalogram

Kozhevnikov, Vadim Andreevich

doi:10.1016/0013-4694(58)90034-8

Cited by 14 publications

(1 citation statement)

References 25 publications

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“…The convention that has emerged in the EEG field is to divide the power spectrum into broad bands of different frequency ranges and refer to each of these bands as 'waves' or 'oscillations' within that range. This definition has its origins in its early history in the 1930s and 40s when the absence of computers necessitated simplifications based on visually recognizable aspects of the signal [1,[9][10][11]. The tedium of performing manual computation on a signal obtained on a film or ink writing oscillograph gave rise to photomechanical frequency analyzers (e.g.…”

Section: Introductionmentioning

confidence: 99%

Characterizing peaks in the EEG power spectrum

Parameshwaran¹,

Thiagarajan²

2019

Biomed. Phys. Eng. Express

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The EEG is an important noninvasive brain signal measured from scalp electrodes with the potential for application in the diagnosis of neurological and mental health disorders. In this context analysis of the spectral properties of the EEG are an important aspect of understanding the signal. One characteristic of the signal that has been observed is the presence of intermittent periodicity or regular oscillatory activity under certain conditions. Typically, though not always, this periodicity occurs at ∼10 Hz (called alpha oscillations) and is observed most reliably under eyes closed conditions as a peak in the power spectrum. In the literature the alpha oscillation is generally characterized by the power in the relevant band of the power spectrum. However, since it occurs embedded within a background of nonperiodic activity with a 1/f γ spectral envelope, this approach does not separate the oscillation from the background nonperiodic activity and therefore provides an imprecise view that is largely determined by the spectral decay rather than the embedded periodicity. Here we present an approach to assess periodicity in the EEG signal that is robust to distortions by changes in the 1/f background nonperiodic activity and is particularly suited for detection of small peaks where the band is overwhelmed by the background decay. The method utilizes the second order derivative of the FFT of the signal autocorrelation to define the resulting metric 'Oscillation Energy' (or E α for alpha oscillations). We demonstrate its performance compared to several variants of traditional power spectrum measures with simulated signals and show its application to example EEG signals. We believe a more precise metric to characterize peaks arising from periodicity like this will enable researchers in the field to develop a clearer understanding of this feature of the signal and its relation to behaviors.

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Section: Introductionmentioning

confidence: 99%