Complex wavelet power spectra were calculated for each epoch by convolving them with complex Morlet wavelets (Lachaux et al., 1999) in 1 Hz increments from 4 to 7 Hz, with a constant time resolution (σt) of 80 ms and frequency resolution (σf) of 2 Hz. Wavelet length was set to constant 500 ms and varied from two to four cycles in the specified frequency range. Complex power spectra were trimmed down to −300 to 800 ms time window, excluding the data points potentially affected by edge artifacts, and downsampled, resulting in 6.7 ms temporal sampling rate. Estimated source power constrained to cortical surface was calculated based on the spectral dynamic statistical parametric mapping approach (Lin et al., 2004), by applying cortically constrained minimum norm estimation procedure (Dale et al., 2000) to the complex wavelet power spectrum. To prevent biasing the inverse solution against spontaneous brain oscillations, the noise covariance used for inverse calculation was estimated from the empty room data pooled across recording sessions and band-pass filtered between 3 and 50 Hz. A signal-to-noise ratio (SNR) of 5 (Lin et
