Computational protocol: Similar Spectral Power Densities Within the Schumann Resonance and a Large Population of Quantitative Electroencephalographic Profiles: Supportive Evidence for Koenig and Pobachenko

Similar protocols

Protocol publication

[…] Brain electrical activity was monitored using a Mitsar 201 amplifier equipped with a 19-channel Electro-Cap International. Measurements from 19 sites (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2) consistent with the International Standard of Electrode Placement were obtained. Impedance for all measurements was maintained below 5 kOhms. The data recorded from the amplifier was delivered to a Dell laptop equipped with WinEEG v.2.8 which produced a digital copy of the recorded voltages. Sixteen-second epochs of eyes-closed data were extracted from each participant and exported into MATLAB software for further filtering and processing.While most data collected with the amplifier were obtained using a sampling rate of 250 Hz, some measurements were collected with a sampling rate of 500 Hz. To insure homogeneity across subjects, data that were collected using a sampling rate of 500 Hz was re-sampled to 250 Hz using the resample.m function within the MATLAB platform. The data for each subject was then filtered between 1.5 and 40 Hz using the eegfiltfft.m function within the freely available EEGLab toolbox []. The function uses an inverse FFT algorithm to band-pass filter raw measurements within a specified frequency range. We have found qualitatively and quantitatively that this filtering algorithm produced identical results independent of whether the segment length was 16 seconds or 120 seconds in duration; the correlation between the raw voltage recordings was 0.996. Once filtered, the data were submitted to spectral analysis, using the spectopo.m function, which computed spectral density within discrete frequencies for the Fp1 O2 T3 and T4 sensors using Welch’s periodogram method employing a window size of 2048 (8.2 seconds) to maximize spectral resolution and a Hamming window with 50% overlap between windows.These data was then imported into SPSS for further analysis and for the computation of mean absolute potential difference along the rostral-caudal (Fp1-O2) axis as well as between the left-right temporal (T3 and T4) sensors. Absolute differences between rostral-caudal and left and right temporal sensors were also obtained by subtracting the absolute raw voltages (independent from spectral density) from the extracted EEG record for each 4-millisecond point interval over 16 seconds. […]

Pipeline specifications

Software tools EEGLAB, SPSS
Applications Miscellaneous, Clinical electrophysiology
Organisms Homo sapiens