## 4 Discussion  
Deep learning (DL) methods have been widely used in various fields, including medical research. In recent years, DL has been applied to acupuncture-related research, providing new insights and understanding into the effects of acupuncture on the human body. The application of DL to acupuncture-related research presents several unique challenges, such as the limited availability of high-quality data, the complex and nonlinear relationships between acupuncture points and physiological responses, and the need to consider the potential biases and confounding factors in the data. Despite these challenges, the application of DL to acupuncture-related research has the potential to greatly advance our understanding of the mechanisms and effects of acupuncture, as well as its clinical applications. By leveraging the power of DL algorithms, researchers can analyse and model large, complex datasets, identify patterns and relationships in the data that are not easily apparent through traditional statistical methods, and make predictions about the effects of acupuncture on various physiological responses. Based on a literature review, the authors of this study provide background information on artificial intelligence as used in EEG analysis, with an introduction to machine learning and deep learning methods for those -- especially clinicians such as acupuncturists and physiotherapists -- who may be unfamiliar with them. Summaries of the advantages and disadvantages of both ML and DL approaches are included, and also of some of their more commonly used algorithms. In addition, a literature review of EEG studies on acupuncture and related modalities was conducted. Based on these reviews, which, in themselves, provide a useful contribution to the literature, the authors used a combination of CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) algorithms, as well as WPD (wavelet packet decomposition) for feature extraction. The experimental set-up was described, including TEAS, EEG data collection and pre-processing. We analysed the EEG data collected in four different ways (_Phases 1 to 4_):  
_Phase 1_. Sixteen hybrid CNN-LSTM models were created in Keras, to examine changes over time during stimulation (Slots 2, 3, 4 and 5) relative to the baseline (Slot 1), for each of the four stimulation frequencies, but without examining the filtered EEG bands separately. This resulted in 2 \(\times\) 2 confusion matrices. _Phase 2_. Twenty hybrid CNN-LSTM models were created in Keras, to examine changes over time (baseline (Slot 1), stimulation (Slots 2-5) and post-stimulation (Slots 6-8)) in each of the five EEG frequency bands (delta, theta, alpha, beta and gamma) for the four stimulation frequencies. This resulted in 3 \(\times\) 3 confusion matrices. _Phase 3_. Twelve scaled conjugate backpropagation MLP-NN models with 10 hidden layers were created using MATLAB, to examine differences between the EEG bands at baseline, and during and following stimulation, for the four stimulation frequencies. This resulted in 5 \(\times\) 5 confusion matrices. _Phase 4_. Here, we reverted to using the CNN-LSTM model in Keras, rather as in Phase 1, but with more LSTM layers. The objective was to examine the same differences as in Phase 3, so that the two very different methods could be compared. Again, this resulted in 5*5 confusion matrices. As with all (unsupervised) DL methods, however useful they may be in identifying and classifying differences, the results are not easy to interpret due to the complexity of the algorithms and the lack of a clear understanding of underlying mechanisms. This can be a major challenge in any study. Another potential challenge is that models may be prone to bias if the training data reflect biased patterns. A third challenge is in determining how to achieve computational efficiency. The Phase 1 analysis appears to show that the greatest differences from the baseline occurred during 80 pps or sham stimulation, and the smallest differences during 2.5 or 10 pps stimulation.