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Free keywords:
EEG; Quantitative; Power spectrum; Coefficient of variation; Reliability; Sample size
Abstract:
The aim of the present study was to evaluate the variability of EEG power spectrum data, considering the time course of the EEG spectrum in resting conditions, and the relationship between the spectral parameters and the length of the analyzed segments. Recordings were performed in 57 normal subjects, with a protocol consisting of regular cycles with open eyes (5 s) followed by closed eyes (55 s) repeated during 10 min. Towards the end of the recording there was a decrease in the alpha and beta power and an increase in the delta and theta power. The coefficient of variation (CV) for the power of 4 s epochs was in the range 0.49–0.67 (delta), 0.53–0.58 (theta), 0.58–0.76 (alpha), 0.37–0.49 (beta) and 0.09–0.12 for the alpha peak frequency. CV decreased with the increase of the sample size, being inversely proportional to the square root of the sample size. Increasing the recording length from 40 to 400 s increased CV by 36% (alpha), 41% (beta), 29% (delta) and 35% (theta), while the standard error of the mean decreased by 55–60%. It is concluded that the power estimates of the EEG activity are heavily dependent on the length of the analyzed segments, and the way they are selected. This observation is particularly relevant for clinical and drug studies where short recordings are often used, thus significantly biasing the estimation of the EEG parameters. The present data provide an estimate on the minimal length of EEG required for a given level of variability.
