## 1 Introduction
(2018) explored the neurophysiological underpinnings of TEAS's effects on brain activity. They employed EEG measurements to assess changes in the EEG power spectrum in response to acupuncture stimulation. The investigation specifically focused on the modulation of ongoing brain activity by acupuncture at the traditional Hegu acupoint compared to a mock point (non-acupoint) under high-frequency (100 Hz) and low-frequency (2 Hz) stimulation. The study recruited 12 healthy male volunteers and utilized a crossover design with two separate sessions. EEG recordings from a 124-electrode system were analyzed to evaluate power changes across various frequency bands. The findings revealed that high-frequency stimulation at the Hegu acupoint resulted in a significant decrease in theta power, particularly at the frontal midline electrodes, specifically Fz and FCz, as well as at electrodes over the contralateral right hemisphere frontal areas, including Fp2, F4, F8, and FC6. This suggests targeted modulation of neural activity in these regions during acupuncture treatment. This effect was absent during low-frequency stimulation and when stimulating the mock point. The attenuation in theta power was transient and observed only during the period of stimulation. Source localization techniques implicated the cingulate cortex and, more specifically, the ACC as the origin of the observed EEG changes. The results of the study suggest that high-frequency acupuncture at specific points can induce acute changes in cortical activity, with potential implications for modulating nociceptive processing. This modulation appears to be frequency- and location-specific, thereby supporting the concept of acupuncture's specificity and its measurable effects on brain function. The employment of a mock point serves as a control to underscore the specificity of the Hegu acupoint's effects, which has implications for the design of future studies examining the neural correlates of acupuncture. In the realm of using deep learning algorithms to analyze the effects of TEAS on EEG signals at different frequencies, the study by [PERSON] et al. (2019) stands out as a pioneering effort. It is the only study to date that employs artificial intelligence (AI), specifically a hybrid convolutional neural network long short-term memory (CNN-LSTM) model, to investigate the effects of different TEAS stimulation frequencies on EEG. Using a 10-20 system with 19 electrodes, their research revealed that the most notable EEG changes from pre-stimulation occurred at higher TEAS frequencies, particularly at 80 and 160 pps. The study also found that different TEAS frequencies might selectively influence certain EEG frequency bands. With the use of the kappa statistic, the CNN-LSTM model demonstrated superior classification accuracy over traditional models like the multilayer perceptron neural network (MLP-NN). Using the same database as ([PERSON] et al., 2019), this study introduced several novel approaches to classifying brain responses to TEAS and distinguishing between the various stimulation frequencies applied. EEGNet, a specialized CNN architecture designed for EEG-based tasks, was employed to implement a four-class classification approach corresponding to four different TEAS frequencies (2.5, 10, 80, and sham). A key contribution of this work was the investigation of the time-based variation of EEG patterns across multiple stimulation phases--pre-stimulation, during-stimulation, and post-stimulation--and capturing the evolving nature of brain responses throughout each 40 min session. This phase-based analysis offered a more comprehensive understanding of how EEG signals modulate in response to different stimulation frequencies. Additionally, saliency maps were applied, to identify the most critical EEG electrodes for classification, enabling the optimization of the electrode setups and reducing the number of electrodes required without sacrificing accuracy. This approach improves system efficiency and will enhance patient comfort in practical, real-world applications.