Continuous EEG data were divided into 2-s half-overlapping segments (i.e., Welch’s method). For each 2-s segment, the mean voltage was subtracted from the data and the resulting mean-centered data were tapered with a 50% Hanning window. The Fast Fourier Transform, as implemented in Matlab, was used to obtain spectral power estimates, which were averaged across all segments. Mean power estimates were calculated for each of four frequency bands: delta (0.5 to 3.5 Hz), theta (4 to 7.5 Hz), alpha (8 to 12.5 Hz), and beta (13 to 30 Hz). The natural logarithm of mean power in each band was used in analyses (cf. Pivik et al., 1993; Pizzagalli, 2007). In addition, we examined the peak (dominant) frequency in the alpha band. For this purpose we padded the (tapered) data with 0s to a series length of 1024, which yielded a frequency resolution of 0.125 Hz, rather than the 0.5-Hz resolution of the original data. We used “gravity frequency” (see Klimesch, 1999 for a discussion), which involves weighting power estimates at each spectral frequency within the alpha band by its corresponding
