## 2 Materials and Methods
### Electrophysiological Measures  
The portable consumer grade device used in this study was the 14-electrode Emotiv EPOC. The research grade device used in this study was the 32-electrode Brainvision system. EPOC was utilized first followed by Brainvision.  
For the Brainvision actiCHamp,EEG activity was first amplified and then sampled at 1 kHz. An online bandpass filter from 1 to 100 Hz was applied. During offline processing, the Brainvision data was downsampled using EEGLAB [63] to 128 Hz to make it compatible with the EPOC sampling rate.  
For the EPOC, EEG signals were initially preprocessed as specified by the manufacturer were high-pass filtered with a 0.16 Hz cut-off, pre-amplified and low-pass filtered at an 83 Hz cut-off. The data were then digitized at 2048 Hz. The digitized signal was further filtered using a 5 th-order sinc notch filter, and down-sampled to 128 Hz following standard practices used within the community of users (for example, see [64]).  
For both systems, the average offline EEG signal was subtracted from each electrode for each time point (offline re-referencing to a common mean reference). The offline averaging was done in a way that allowed separate averages for the **Event-Related Band Potentials (ERBPs)** in each experimental condition (such as the different tasks described below in Sections 2.3 and 2.4) and stimulus type for electrodes with epochs ranging from \(-200\) ms pre-stimulus to 1000 ms post-stimulus. The trials where excessive peak-to-peak deflection occurred at non-ocular electrode placements were considered contaminated and excluded from the averages. Following artefact correction and removal, less than 15% of trials were rejected.  
To extract the EEG features, we applied the Fast Fourier Transform (FFT) algorithm to transform time recordings into frequency recordings in order to compute the continuous input EEG data for each condition. We tabulated and computed the total spectral content of the electrical activity of each electrode, using a \(-1024\) to 1024 ms time window. We computed FFTs for each participant on a grand average for each condition (high arousal, low arousal, high valence, low valence) on both the ANEW and IAPS tasks, and low and high load for the two versions of the n-Back task. FFT computation was performed through the BESA spectral analysis pipeline ([[http://wiki.besa.de/index.php?title=BESA_Research_Spectral_Analysis](http://wiki.besa.de/index.php?title=BESA_Research_Spectral_Analysis)]([http://wiki.besa.de/index.php?title=BESA_Research_Spectral_Analysis](http://wiki.besa.de/index.php?title=BESA_Research_Spectral_Analysis)), accessed on 20 May 2022). The FFT was applied to the marked regions of the blocked data windows to examine the frequency content of the signal. FFTs were normalized and the frequency power and amplitude bands from 1-50 Hz were exported for further analysis (FFT was viewed as amplitude and power in frequency bands, and the tabulated results were saved in ASCII data files). The power Band frequencies were defined as follows: Delta (1.0 Hz-4.0 Hz), Theta (4.0 Hz-8.0 Hz), Alpha (8.0 Hz-14.0 Hz), Beta (14.0 Hz-30.0 Hz), Gamma (30.0 Hz-50.0 Hz).