## 2 Materials and Methods
### Data Preprocessing  
The preprocessing pipeline began with band-pass filtering the data to isolate the frequency range of interest, specifically between 0.5 and 45 Hz, using second-order Butterworth filters implemented in MATLAB(Version: R2024a). This choice was made due to the Butterworth filter's maximally flat frequency response, ensuring minimal amplitude distortion and efficient computational performance. This filter effectively attenuates frequencies outside this range, including the interference from the UK's 50 Hz power supply. Following the initial filtering, an independent component analysis (ICA), utilizing the extended Infomax algorithm, was employed to identify and remove non-neural artifacts from the EEG data [23]. This step was complemented by the use of the multiple artifact rejection algorithm (MARA), to further enhance the artifact rejection process [24]. The identification of artifactual components was refined using ICLabel [25], an EEGLAB plugin [26], which facilitated the labeling and subsequent removal of these components. ICLabel leveraged a pre-trained neural network to classify independent components into categories such as brain, muscle, eye, heart, line noise, channel noise, and others, based on their spatial, spectral, and temporal characteristics. Subsequently, those marked \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{5}{c}{**Demographic Features**} \\ \hline
**Feature** & **Male** & **Female** & & \\ Sex & 18 & 30 & & \\ Age & & & Mean (SD) [Min, Max] & & \\  & & 43.55 (16.98) [18; 69] & & \\ \hline \multicolumn{5}{c}{**Clinical Features**} \\ \hline
**Feature** & **Mean** & **SD** & **Min** & **Max** \\ Pre-anxiety & 15.70 & 19.61 & 1 & 100 \\ Post-anxiety & 8.74 & 14.87 & 3 & 80 \\ Pre-comfort & 67.17 & 21.71 & 0 & 101 \\ Post-comfort & 65.76 & 23.58 & 10 & 101 \\ Pre-relaxation & 68.31 & 20.66 & 0 & 100 \\ Post-relaxation & 70.91 & 22.60 & 1 & 101 \\ Pre-liveliness & 46.40 & 23.88 & 1 & 101 \\ Post-liveliness & 34.99 & 25.90 & 1 & 95 \\ Pre-fatigue & 31.11 & 23.70 & 2 & 89 \\ Post-fatigue & 37.53 & 26.31 & 3 & 100 \\ Pre-gloominess & 12.91 & 16.24 & 2 & 76 \\ Post-gloominess & 9.71 & 17.07 & 3 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Baseline demographic, pre-stimulation, and post-TEAS session clinical characteristics of the participants, based on the numerical rating scale for mood (NRS-M). Figure 2: EEG data for Channel 1 across all slots, illustrating the pre-stimulation, during-stimulation, and post-stimulation phases. The slots are color-coded, and the horizontal axis indicates the first second of recording for each slot, for better illustration. artifactual components were mathematically subtracted from the data, and the remaining components, primarily reflecting brain activity, were projected back to the scalp electrode space to reconstruct the EEG signals. This resulted in a cleaned dataset with reduced artifact contamination, suitable for further analysis. Epochs exhibiting significant deviations in amplitude were identified and trimmed using the TrimOutlier EEGLAB plugin. The threshold for outlier removal was set at \(\pm\)3 standard deviations from the mean amplitude across all electrodes [19]. The final stage of preprocessing involved re-referencing the data, using a current source density (CSD) transformation, implemented via the CSD Toolbox in MATLAB, to apply a Laplacian montage, which provides a more localized representation of scalp electrical activity [27]. To augment the database, each 10 s epoch was divided into 1 s trials, with each trial having dimensions of 19 electrodes by 500 time samples. The number of trials for each distinct stimulation frequency across the eight slots was approximately as follows: for 2.5 pps, it ranged from 4740 to 4800 trials; for 10 pps, from 4750 to 4800 trials; for 80 pps, from 4750 to 4800 trials; and for the Sham condition, from 4750 to 4800 trials. The number of trials for each frequency remained relatively stable across the slots, ensuring a balanced dataset for subsequent analysis.