### Literature Review and Resulting Proposed Strategy  
Combinations and comparisons of ML and DL algorithms were located in PubMed-indexed papers using the search terms 'EEG AND [ML A] AND [DL A],' where [ML A] and [DL A] are the standard acronyms for the DL and ML algorithms, respectively. The exceptions were PCA (Principal Component Analysis) and RF (Random Forest), which were searched using their full names. The results of the searches conducted on 5 December 2021 are shown in Table 1.  
Figure 1: Timelines of research on deep learning (DL), machine learning (ML), acupuncture (Acup), electroacupuncture (EA), transcutaneous electrical nerve stimulation (TENS) and transcutaneous electroacupuncture stimulation (TEAS), 2000–2021. Based on PubMed searches, 6 December 2021.  
Table 2 shows the results of the PubMed searches for combinations or comparisons of the two DL algorithms, carried out on 5 December 2021.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**DL Algorithm** & **ML Algorithm** & **Counts (All)** & **Combinations** & **Comparisons** & **Other/Unclear** \\ \hline \multirow{5}{*}{CNN} & SVM & 29 & 9 & 20 & 0 \\  & RF & 17 & 4 & 10 & 3 \\  & LDA & 7 & 0 & 0 & 0 \\  & LR & 5 & 1 & 0 & 1 \\  & Clustering & 10 & 3 & 0 & 6 \\  & PCA & 1 & 0 & 0 & 0 \\ \hline Sum & & 69 & 17 & 30 & 10 \\ \hline \multirow{5}{*}{LSTM} & SVM & 15 & 5 & 10 & 0 \\  & RF & 7 & 1 & 6 & 0 \\  & LDA & 3 & 1 & 2 & 0 \\  & LR & 0 & 0 & 0 & 0 \\  & Clustering & 2 & n/a & n/a & 2 \\  & PCA & 2 & 1 & 1 & 0 \\ \hline Sum & & 29 & 8 & 19 & 2 \\ \hline \multirow{5}{*}{RNN} & SVM & 6 & 1 & 3 & 2 \\  & RF & 5 & 1 & 3 & 1 \\  & LDA & 1 & 0 & 1 & 0 \\  & LR & 0 & 0 & 0 & 0 \\  & Clustering & 1 & n/a & n/a & 1 \\  & PCA & 0 & 0 & 0 & 0 \\ \hline Sum & & 14 & 2 & 7 & 4 \\ \hline \multirow{5}{*}{DNN} & SVM & 6 & 4 & 2 & 0 \\  & RF & 2 & 1 & 1 & 0 \\  & LDA & 2 & 1 & 1 & 0 \\  & LR & 0 & 0 & 0 & 0 \\  & Clustering & 2 & 2 & 0 & 0 \\  & PCA & 0 & 0 & 0 & 0 \\ \hline Sum & & 11 & 8 & 4 & 0 \\ \hline \multirow{5}{*}{DNN} & SVM & 56 & 19 & 35 & 2 \\  & RF & 31 & 7 & 20 & 4 \\ \cline{1-1}  & LDA & 13 & 2 & 11 & 0 \\ \cline{1-1}  & LR & 5 & 1 & 3 & 1 \\ \cline{1-1}  & Clustering & 15 & 5 & 1 & 9 \\ \cline{1-1}  & PCA & 3 & 1 & 2 & 0 \\ \hline \end{tabular} Note: Clustering (or obvious synonyms) did not appear in some papers located in PubMed when using the search term ‘cluster’, while in some papers that did include clustering, it did not appear to be used as an ML method. For Logistic Regression (LR), one study did not provide results for either the combination or comparison of methods [16] NB: Not all (non-feature-based) DL methods outperformed (feature-based) ML methods (see some of the Support Vector Machine vs. Convolutional Neural Network (SVM vs. CNN) studies, for example).  
\end{table}
Table 1: Combinations and comparisons of ML and DL algorithms (A) located in PubMed-indexed papers using the search terms ‘EEG AND [ML A] AND [DL A].’ Combination and comparison counts were taken from study abstracts. Where this classification was not obvious, counts are included in the ‘Other/Unclear’ column. Abbreviations used: CNN: Convolutional Neural Network; DNN: Deep Neural Network; LDA: Linear Discriminant Analysis; LR: Logistic Regression; LSTM: Long Short-Term Memory; PCA: Principal Component Analysis; RNN: Recurrent Neural Network; SVM: Support Vector Machine. These terms are explained in more detail in the online Supplementary Materials (SM1).  
CNN-LSTM models are thus relatively common hybrids and are more common than LSTM-CNN, the inverse combination. CNN-RNN and LSTM-RNN are the next most common hybrid models. This was confirmed for DL algorithms in general (i.e., not restricted to EEG studies), using Google Scholar instead of PubMed (the abbreviations are defined in the caption of Table 1, and explained in the online Supplementary Materials, SM1). CNN-LSTM thus appears to be an appropriate hybrid model for the current study. For those unfamiliar with CNN and LSTM, a description is provided in the online Supplementary Materials (SM2.1 and SM2.2).