## 2 Materials and Methods
### EEG Data Processing  
A 40-channel Neuroscan QuikCap EEG machine (Compumedics USA 144 Ltd., El Paso, TX, USA) with sintered Ag/AgCl electrodes was used to record EEG signals with electrode arrangement in consonance with the international 10/20 system and aligned with the anatomical inion and nasion points. A NuAmps digital amplifier (Compumedics USA Ltd., El Paso, TX, USA) was used for signal acquisition and digitalization at a sampling rate of 1000 Hz and passed through a bandpass filter of DC to 250 Hz. The amplifier was connected to the Curry 7 Acquisition software (Compumedics USA Ltd., El Paso, TX, USA) running on a Dell Optiflex 9020 desktop PC. EEG recordings were initially referenced to the average of the A1-A2 earlobe electrodes, and subsequently converted to a common average reference offline. By using the four electrodes, EOG data were collected. Specifically, two electrodes were placed above and below the left eye to measure vertical eye movement, and the other two electrodes were located on the outside left and right cannus to measure horizontal eye movement. Prior to the commencement of EEG recording, the electrode impedance values were adjusted to <5 k\(\Omega\) at all the electrodes to ensure the quality of signal acquisition. The EEG signals were processed using a 1-45 Hz 2 nd order Butterworth bandpass filter, then re-referenced to a common average. Data tapering was performed using a Hann window with a 10% width to prevent data loss. This was then followed by a visual examination of the EEG signals to identify artefacts (eye movements, muscle movements, spontaneous discharges or electrode pops etc. ), which were then removed from the recorded data. Bad block and eye blink detection (using the magnitude of eye blink deflections as a set threshold criterion to detect artefacts) was undertaken by three automated methods (subtraction, covariance and principal component analysis) to produce clean EEG data. From the cleaned EEG data generated, 2 secs back-to-back epochs were then created and re-examined such that epochs with bad blocks were removed from averaged data; in this study, all the participants had a minimum of 87% usable epochs in the Eyes Open condition. By using the Key Institute eLORETA (exact low-resolution brain electromagnetic tomography) [35] software, the functional lagged linear connectivity (also known as coherence) estimates of four EEG frequency band activity were obtained, i.e., theta (4.05-8 Hz), alpha (8.05-12 Hz), beta (12.05-30 Hz), and gamma (30.05-45 Hz). This technique provides a single weighted minimum norm solution to the inverse problem and has been demonstrated to provide zero error (despite low spatial resolution) in localising cortical grey matter test sources [36; 37]. The weights utilised by eLORETA (based on the EEG montage used in the recording) are used to calculate the current source density throughout the grey matter of a standardised realistic head model [38] based on the MNI template [39]. The resulting current density distribution is used to calculate measures of linear dependence between 'virtual' electrodes placed at regions of interest (ROIs) within the grey matter; for a comprehensive description of the mathematics underlying the eLORETA methodology and how the weighted norms are calculated, see [35; 40]. ROIs were selected using commonly identified grey matter nodes in the DAN, VAN, and SN based on MNI coordinates identified in previous studies [41; 42], with all the grey matter tissue within 10 mm of the identified source included as part of that node. This resulted in 15 ROIs being selected (see Table 1). In Table 1, 'Location' refers to the generally identified brain region. However, MNI coordinates locate each region more specifically.