EEG Data Post Stimulation/Sham (All Three Conditions)

EEG signal data (20 channels) was acquired using a Quick-30 EEG headset (Cognionics Inc.) with data recorded using proprietary acquisition software. Impedance was set to below 10 kOhm. All electrodes were amplified by a factor of 1,000x and sampled at 500 Hz. Online bandpass filtering was set at 0.3–100 Hz (half-amplitude, 3 dB/octave roll-off. A cloth EEG cap, with pre-set electrode locations was placed on the subject and measured to position the Cz site over the subjects actual Cz location. This was used to compute measurements for the placement of stimulation electrodes using the 10-20 EEG system following Meinzer et. al. (2014).EEG data were collected from twenty scalp electrodes (reference at earlobes) and processed using the EEGLAB toolbox (Delorme & Makeig, 2004) within the MATLAB processing platform (The Mathworks, Inc.). Data from the twenty relevant channels were selected and channel locations identified using Cognionics supplied location file. EEG channel data was first filtered using a linear finite impulse response (FIR) filter with a bandpass of 0.5 Hz to 40 Hz. For baseline and DAP task data, noisy outlier channel values were rejected using the manual data scrolling review function until data reached a 50 (+/- 10) microvolt vertical scale limit. For face recognition data, epochs (-1000 to 2000 ms) were extracted following the time-locking event. Mean baseline values ( -1000ms to 0ms) were subtracted from each epoch to remove DC drifts. Following visual inspection, all data sets were re-referenced to the average. Next, an Independent Component Analysis (ICA) was applied (RUNICA uses the infomax ICA algorithm of Bell & Sejnowski (1995), with the natural gradient feature of Amari, Cichocki & Yang (1999), and the extended-ICA algorithm of Lee, Girolami & Sejnowski (1999). It is used to separate the preprocessed EEG signals into independent components and found to be effective in removing EOG and EMG artifacts, noise, as well as separating EEG cerebral sources, including mu components (Romero, Mananas, & Barbanoj, 2008; Ng & Raveendran, 2009; Gómez-Herrero et al., 2006). The RUNICA algorithm was applied at least twice during the processing of each dataset. In the first step, RUNICA was used similarly to the procedures described by Ng and Raveendran (2009) to extract and remove components that contained a large portion of noise and artifacts, including ocular artifacts. Next, “cleaned” EEG data were reconstructed from the remaining non-artifactual components. RUNICA was applied a second time to the cleaned EEG data so that an equal number of IC components comprised all the datasets. Sequential application of RUNICA yielded data with improved signal to noise ratio, by unmixing and removing standard EEG artifacts from the brain activity data. This overall approach was found to yield the most robust results.Event related potentials and event related spectral perturbations. Event related potentials (ERPs) and event related spectral perturbations (ERSPs), deviations in amplitude and spectral power relative to a baseline, respectively, were calculated for faces in the STIM and SHAM conditions, using built-in EEGLAB procedures (Delorme & Makeig, 2004). A time-frequency decomposition was computed for each individual condition using wavelets with Morlet tapers, and the deviations in log spectral power in each time-frequency bin were then computed, relative to the mean of the log spectral power of the 1000 ms pre-stimulus baseline. To compare responses for specific experimental conditions, the common baseline was calculated across those test conditions, and the component ERSP values were adjusted for the common baseline for each test. To assess statistical differences, nonparametric resampling methods were used (Manly, 2007). A bootstrap resampling method was used to test whether ERP and ERSP deviations in the post-stimulus interval were significantly larger relative to the pre-stimulus period for each subject and each separate condition.