## 4 Discussion  
The findings from our study verify and extend the work by [PERSON] et al. (2019), which pioneered the application of artificial intelligence (AI) to understand the effects of TEAS stimulation on EEG signals. Our research advances the field by not only employing a \begin{table}
\begin{tabular}{l l l} \hline \hline
**Name of Combinations** & **Combination** & **Name of Electrodes** \\ \hline comb1 & 1, 2 & Fp1, Fp2 \\ comb2 & 1, 2, 5 & Fp1, Fp2, Fz \\ comb3 & 1, 2, 3, 5, 7 & Fp1, Fp2, Fz, F8, F7 \\ comb4 & 1, 2, 3, 5, 7, 8, 12 & Fp1, Fp2, Fz, F8, F7, T3, T4 \\  & & Fp1, Fp2, F7, F3, Fz, F4, F8, \\ all electrodes & 1–19 & T3, C3, Cz, C4, T4, T5, \\  & & P3, Pz, P4, T6, O1, O2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Electrode combinations and their corresponding electrode label; “comb” denotes the combinations of electrodes used for classification. Figure 5: Topographic distribution of aggregated saliency maps for various TEAS frequencies. The color scale indicates the relative importance of each electrode’s signal in the model’s classification, with warmer colors indicating higher saliency. Figure 6: Aggregated classification accuracy of the three phases for incremental electrode combinations. cutting-edge convolutional neural network model, EEGNet, but also by achieving high classification accuracies. The EEGNet model achieved over 95% classification accuracy in detecting brain responses to various TEAS frequencies, with accuracies consistently high across different time slots during stimulation (95.65% to 97.66%). This high level of accuracy suggests that the model is proficient in distinguishing between the different TEAS frequencies, indicating that TEAS at various frequencies may engage different neural mechanisms and pathways. This knowledge could help tailor TEAS treatments to achieve specific therapeutic effects, such as pain management or cognitive enhancement. Furthermore, the classification accuracies across the pre-stimulation, during-stimulation, and post-stimulation phases were consistently high (above 92%), demonstrating that EEGNet can effectively capture time-based brain responses throughout different stimulation phases. This provides a detailed view of how TEAS affects brain activity over time, which is crucial for optimizing therapeutic strategies and understanding the immediate and long-term neural effects of TEAS. In terms of demographic and clinical factors, the study showed that EEGNet's classification accuracy remained high across different groups, including variations in sex, age, and psychological states. The accuracy for female participants ranged from 93% to 100%, while for male participants it ranged from 90% to 95%. Age groups also showed high accuracies, with older adults (over 45 years) ranging from 92% to 98% and younger adults (under 45 years) from 90% to 99%. Additionally, the model demonstrated that high relaxation states achieved accuracies between 90% and 100% and that low relaxation states ranged from 90% to 97%. Similarly, high anxiety levels corresponded to accuracies of 90% to 100%, while low anxiety levels ranged from 90% to 97%. This robustness across demographics and psychological states suggests that the model is reliable and could benefit a broad range of patients. Additionally, the responsiveness of different EEG frequency bands to TEAS was investigated. The beta and gamma frequency bands exhibited the highest responsiveness, with classification accuracies exceeding 90%. This indicates significant effects on higher cognitive functions, aligning with previous studies suggesting that beta and gamma activities are associated with cognitive processing and neural synchrony. Finally, the use of saliency maps revealed that a subset of electrodes (Fp1, Fp2, Fz, F7, F8, T3, T4) could achieve accurate classification. This finding is significant, as it suggests the potential for more efficient EEG setups, reducing the number of electrodes required without compromising accuracy.