### Literature Review of AI, Acupuncture and EEG  
A brief review was conducted of studies indexed in three major online databases--PubMed, SCOPUS and CNKI--on machine learning (ML), Support Vector Machines (SVM), deep learning (DL) or Convolutional Neural Networks (CNN) and electroencephalography (EEG), acupuncture (Acup) or electroacupuncture (EA). SVM and CNN were selected as frequently used exemplars of ML and DL, respectively. The results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**DL Algorithm** & **ML Algorithm** & **Counts (All)** & **Combinations** & **Comparisons/Other** \\ \hline \hline \multirow{4}{*}{CNN} & [CNN] & \multirow{4}{*}{44} & \multirow{4}{*}{30} \\  & LSTM & & & & \\  & RNN & & & \\  & DNN & & & \\ \hline Sum & & 68 & 6 & 62 \\ \hline \hline \multirow{4}{*}{RNN} & CNN & 13 & 0 & 13 \\  & LSTM & 17 & 1 & 16 \\  & [RNN] & & & \\  & DNN & & & \\ \hline Sum & & 31 & 2 & 29 \\ \hline \multirow{4}{*}{DNN} & CNN & & & \\  & LSTM & 11 & 0 & 11 \\  & RNN & 7 & 0 & 7 \\  & [DNN] & 1 & 1 & 0 \\ \hline Sum & & 19 & 1 & 18 \\ \hline \end{tabular}
\end{table}
Table 2: Results of PubMed searches for combinations or comparisons of two DL algorithms (for abbreviations, see caption of previous table).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c|}{**PubMed**} & \multicolumn{4}{c|}{**SCOPUS**} & \multicolumn{4}{c}{**CNKI**} \\ \cline{2-13}  & **ALL** & **EEG** & **Ac** & **EA** & **ALL** & **EEG** & **Ac** & **EA** & **ALL** & **EEG** & **Ac** & **EA** \\ \hline Machine learning & \#1.88\%\(\rightarrow\)3 & 60,085 & 1711 & 30 & 1 & 315,567 & 3957 & 38 & 3 & 290,855 & 1516 & 34 & 2 \\ \hline Support Vector Machine & \multirow{4}{*}{18,367} & \multirow{4}{*}{1277} & \multirow{4}{*}{15} & \multirow{4}{*}{0} & \multirow{4}{*}{145,475} & \multirow{4}{*}{4617} & \multirow{4}{*}{37} & \multirow{4}{*}{2} & \multirow{4}{*}{114,018} & \multirow{4}{*}{583} & \multirow{4}{*}{14} & \multirow{4}{*}{2} \\  & & & & & & & & & & & \\ \hline Deep learning & \#1.8\%\(\rightarrow\)3 & 25,456 & 627 & 5 & 0 & 165,435 & 1982 & 2 & 1 & 213,990 & 786 & 28 & 7 \\ \hline Convolutional Neural & \multirow{4}{*}{13,580} & \multirow{4}{*}{480} & \multirow{4}{*}{4} & \multirow{4}{*}{0} & \multirow{4}{*}{102,937} & \multirow{4}{*}{1659} & \multirow{4}{*}{5} & \multirow{4}{*}{2} & \multirow{4}{*}{125,390} & \multirow{4}{*}{676} & \multirow{4}{*}{8} & \multirow{4}{*}{0} \\ Network & & & & & & & & & & & \\ \hline Acupuncture & \#1.8\%\(\rightarrow\)3 & 34,611 & 309 & & & 50,417 & 288 & & 194,603 & 463 & & \\ \hline Electroacupuncture & \#1.8\%\(\rightarrow\)3 & 6350 & 69 & & & 9267 & 62 & & 44,088 & 122 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numbers of studies located on 5 December 2021 in PubMed, SCOPUS and CNKI on machine learning (ML), Support Vector Machines (SVM), deep learning (DL) or Convolutional Neural Networks (CNN) and electroencephalography (EEG), acupuncture (Ac) or electroacupuncture (EA).  
The percentage of all ML studies that mention SVM was thus lowest in PubMed (30.57%) and highest in SCOPUS (46.10%). Similarly, the percentage of all DL studies that mention CNN was lowest in PubMed (53.35%) and highest in SCOPUS (62.22%). In both PubMed and SCOPUS, a greater percentage of EEG studies mentioned SVM than CNN, but this was reversed in CNKI, suggesting geographical bias.