## 3 Results  
For the evaluation of our results, we record a series of metrics (accuracy, sensitivity, specificity, precision, and AUC), but the basic conclusions are based mainly on the metric of accuracy. Accuracy is a qualitative performance metric that gives a measurement's proximity to the actual value [41]. We rely on this metric because our study consists of binary classification where the numbers of items of the two classes do not differ significantly. Moreover, accuracy includes other metrics (precision and trueness) and is a function of these metrics. Finally, considering the other metrics does not alter the conclusions of our study. The training time, i.e., the computational cost of each model, is also recorded to give us a metric of the models' complexity. The results of the first four groups of experiments are shown in Table 1, where the best performance for each experiment group is indicated by bold characters. Although it is important that a simple five-hidden-layer FFNN (Exp. 1 and 2) applied on the 256 sample values of the MEG segments can achieve 88.1% accuracy with barely an hour of training, this is clearly improved by using the ST-FFT spectral values as input to the FFNN models, an improvement present in all metrics (Exp. 3, 4, and 5). Slightly better results are obtained when we have both the signal values and the ST-FFT values combined as an input, but with an enormous cost in training time that exceeds 30 h (Exp. 6 and 7). For the purpose of comparison, representative results obtained by the use of 1D-CNN applied on 1-D MEG Signal are shown, with size and structure details described in the last column of Table 1 (Exp. 8, 9, 10, 11, and 12). We summarize the results of the ANN model enhancement in Table 2. The third column shows both the accuracy of the initial pretrained FFNN and the resulting accuracy of the new, enlarged model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Experiment** & **Exp \#** & **Accuracy** & **Sens** & **Spec** & **Prec** & **AUC** &
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} & **Structure/Size** \\ \hline Group V—Multiple-Output 2 & 13 & 90.99–90.9 & 89.4 & 91.7 & 89.6 & 0.9660 & 4 h & 16 outputs \(\rightarrow\) [32 32 32] \\ Sequential FFNNs & 14 & 90.99–91.1 & 86.8 & 92.9 & 86.8 & 0.9680 & 4 h & 32 outputs \(\rightarrow\) [32 32 32] \\ \hline Group VI—Single-Output 2 & 15 & 91.9–92.5 & 94.1 & 91.4 & 86.8 & 0.9720 & 3 h & [64 32 32 16] \(\rightarrow\) [64 32 16] \\ Sequential FFNNs & 16 & 91.9–92.6 & 93.7 & 92.0 & 87.7 & 0.9636 & 3 h 43 min & [64 32 32 16] \(\rightarrow\) [64 32 16] \\ Sequential FFNNs & 17 & **91.9–92.65** & 93.3 & 92.3 & 88.2 & 0.9688 & 2 h & [32 32 16] \(\times\) 5 \\ \hline Group IX—Single Output & 18 & 91.73–91.5 & 93.1 & 90.6 & 85.5 & 0.9627 & 30 h & 5\(\times\) [32 16 8] \(\rightarrow\) 5 Inputs [32 16 8] \\ Parallel Configuration & 19 & 91.73–92.6 & 93.7 & 92.0 & 87.7 & 0.9651 & 30 h & 5\(\times\) [32 16 8] \(\rightarrow\) 521 Inputs [32 16 8] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results of the experiments using ANN model enhancement by utilizing pretrained FFNNs. Bold numbers indicate the best results.