## 3 Results
### Impact of Demographics and Clinical Factors on EEGNet's Performance During TEAS  
The study further explored the impact of personal characteristics--specifically, sex, age, and psychological state--on the classification accuracy of EEGNet in differentiating brain activity across the pre-stimulation, during-stimulation, and post-stimulation phases. The classification accuracy of the EEGNet model across different demographic factors indicated a high level of robustness and consistency. Here are the key observations from the results:  
For female participants, the classification accuracy ranged from 93% to 100% (Table 4). This high accuracy suggests that the model performs exceptionally well in classifying EEG signals for females. For male participants, the accuracy ranged from 90% to 95%. Although slightly lower than for females, the accuracy was still within a high range, demonstrating the model's reliability. The complete classification performance for these two groups is provided in Tables S3 and S4 in the Supplementary Material. The influence of age on the classification accuracy of EEGNet was examined by dividing the participants into two equal age groups. The median age of the participant pool was determined to be 45 years, resulting in two cohorts: 25 participants aged over 45 and 23 participants aged under 45. The participants aged over 45 years showed classification accuracies between 92% and 98% (Table 4). This indicates that the model can effectively classify EEG signals in older adults. For participants under 45 years, the accuracy ranged from 90% to 99%, which is comparable to the older group, suggesting that age does not significantly impact the model's performance. The complete classification performance for these two groups is provided in Tables S5 and S6 in the Supplementary Material. The study further investigated the impact of psychological states, specifically relaxation and anxiety levels, on the classification accuracy of EEGNet for different TEAS frequencies across time slots. To achieve this, participants were first grouped based on their self-reported relaxation and anxiety scores. The dataset was processed to calculate pre-stimulation and post-stimulation scores for relaxation and anxiety. The 'pre-stimulation relaxation' and 'post-stimulation relaxation' scores were computed by summing up the scores of specific items from the NRS-M that corresponded to the participant's state of liveliness, comfort, and relaxation before and after the stimulation, respectively. Similarly, the 'pre-stimulation anxiety' and 'post-stimulation anxiety' scores were calculated by summing up the items related to anxiety, fatigue, and gloominess. To quantify the change in relaxation and anxiety levels due to the stimulation, the percentage change for each participant was calculated as follows:  
\[\text{Change}=\left(\frac{\text{Post}-\text{Pre}}{\text{Pre}}\right)\times 100. \tag{17}\]  
The participants were then grouped into 'high' and 'low' categories based on whether their percentage change was above or below the value of 50%. Those with a relaxation percentage change of 50% or higher were placed in the'relax high' group, indicating a significant increase in relaxation levels post-stimulation. Conversely, participants with less than a 50% change were categorized into'relax low', suggesting a lower increase in relaxation. The same approach was applied to anxiety levels. Participants with a percentage change of 50% or higher in anxiety scores were assigned to the 'anx high' group, indicating a significant increase in anxiety post-stimulation. Those with less than a 50% change were placed in the 'anx low' group, reflecting a lower increase or a decrease in anxiety levels. With these groups established, the EEG data were then analyzed to determine whether the psychological state of relaxation or anxiety had any significant effect on the brain's response to TEAS frequencies. High relaxation levels corresponded to classification accuracies between 90% and 100% (Table 4).