## 5 Conclusions  
The application of DL to acupuncture-related research is a step change in the field and 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. This study is the first of its kind to use artificial intelligence to explore the effects of TEAS frequencies on EEG. From the published literature, no AI research appears to have been conducted into the effects on EEG of different frequencies of electroacupuncture-type stimulation (whether EA or TEAS), although there are several studies on the effects of manual needle rotation frequency from Tianjin University. Additionally, from the published literature, both WPD and the hybrid CNN-LSTM model appear to be appropriate methods of examining the central (EEG) effects of peripheral stimulation (TEAS). Using these methods, we found--contrary to expectation--that the greatest differences in EEG from baseline occurred during 80 pps or the'sham' (160 pps) TEAS applied to the hands), with a mean _kappa_ of 0.454 and 0.467, respectively, while the smallest differences occurred during 2.5 or 10 pps stimulation (mean _kappa_: 0.393 and 0.360). On the other hand, when taking the EEG bands into account, the greatest differences among Slot 1 (baseline)/Slots 2-5 (stimulation)/Slots 6-8 (post-stimulation) occurred for 2.5 pps TEAS in the Theta, Alpha and Gamma bands, and for 80 pps TEAS in Alpha (mean _kappa_ 0.506). Even higher values of _kappa_ were obtained from differences among the EEG bands before, during and after TEAS at different frequencies, but this result was difficult to interpret and explain, and warrants further exploration in future studies. Future DirectionsThere are many potential avenues for further research based on the findings of this paper. Possible approaches could include conducting additional experiments to confirm or refute the findings of this study, as well as using different algorithms, different frequencies of TEAS, or different subject populations. Further research is planned using conventional methods of EEG analysis, different frequency bands (e.g., narrow bands centred on the stimulation frequencies), as well as ML methods based on careful feature selection, in order to see if the results obtained here can be replicated or improved--or, indeed, explained. Such features will include several connectivity (or graph theoretical) measures, includingthose of a source localisation method such as sLORETA (standardised low-resolution brain electromagnetic tomography), to investigate whether findings are due to neural or volume conduction, or, indeed, both. Changes over time both _during_ and _after_ stimulation should also be investigated for different TEAS frequencies. Changes due to volume conduction effects would only occur _during_, not _after_ stimulation. Because of the potentially large number of features that could be examined, automated feature selection is an option for use in this further investigation. EEG cordance and some topological measures have been computed for the current dataset. Although these results remain unpublished as yet, they may also be useful in guiding feature studies. Furthermore, particular attention could be paid to entropy measures, whether in the time, frequency or spatial domain, as well as wavelet-based entropy using different entropy estimators, such as discrete wavelet entropy or permutation entropy. These entropy measures could potentially provide useful insights into the effects of different frequencies of TEAS on the brain, by quantifying the changes in the degree of disorder or uncertainty in the EEG signal.