# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 4 Experimental Results
### Simulation Results

Table 2: A comparison of mental state classification results. The results show a success rate in the range from 94.7% to 98.3% for the subject-specific case (indicated diagonally on the table) and an average accuracy of 92.53% for the transfer learning model. The generic model demonstrated comparable results, with a 92.64% success rate on average. These findings underscore the model's ability to effectively classify situational awareness based on EEG data for both training paradigms. Figure 7 depicts the topography maps of the EEG signal magnitude divided into five frequency bands (delta, theta, alpha, beta, and gamma) for each of the three mental states (_focused_, _unfocused_, and _drowsy_). It can be seen that both the _focused_ and _unfocused_ mental states were mainly characterized by the delta and theta channels (i.e., in the low-frequency EEG activity, specifically within the 1-8 Hz range). However, while in the _focused_ state, there was increased activity in the frontal lobe, and in the state of lack of focus, there was a decrease in activity in this area. The drowsing state was characterized mainly by the alpha channel sub-band (8-14 Hz). This outcome is consistent with existing research findings on the correlation between the alpha EEG band and the _drowsy_ mental state [24]. Figure 8 depicts the timely activity examined in the _F3_ EEG channel located in the frontal lobe for the delta sub-band. The spike rate accurately corresponded to the three mental states and demonstrated an activity decrease for the _unfocused_ state compared with the _focused_ state. We evaluated the relative contribution of the various EEG electrodes in discerning the three mental states (_focused_, _unfocused_, and _drowsy_). Remarkably, we discovered that the best classification performance could be achieved when using only five EEG electrodes: _F7_, \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Trained**} \\ \cline{2-9}  & & **P0** & **P1** & **P2** & **P3** & **P4** & **Avg TL** & **Generic Model** \\ \hline \multirow{5}{*}{**Deep Learning**} & **P0** & 98.3\% & 91.1\% & 88.5\% & 93.8\% & 97.3\% & 92.68\% & 93.4\% \\ \cline{2-9}  & **P1** & 91.3\% & 94.7\% & 87.3\% & 92.4\% & 90.2\% & 90.3\% & 89.8\% \\ \cline{2-9}  & **P2** & 93.9\% & 85.1\% & 95.9\% & 88.1\% & 89.3\% & 89.1\% & 94.3\% \\ \cline{2-9}  & **P3** & 91.2\% & 92.8\% & 95.2\% & 97.2\% & 96.3\% & 93.88\% & 93.5\% \\ \cline{2-9}  & **P4** & 89.9\% & 87.5\% & 92.5\% & 96.8\% & 97.9\% & 91.68\% & 92.2\% \\ \hline \multirow{2}{*}{**Deep Learning**} & **Avg** & 92.92\% & 90.24\% & 91.88\% & 93.66\% & 94.2\% & 92.53\% & 92.64\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Transfer learning results. This table summarizes the simulation results when the network was trained on data from one subject and tested across all participants. The right column shows the performance of the generic model, which was trained using data collected from all participants and then tested using each of the subjects separately. Figure 7: EEG topography maps for the five EEG sub-bands (delta, theta, alpha, beta, and gamma). \(F3\), \(AF3\), \(F4\), and \(AF4\). Three of those five electrodes were significantly situated over the frontal lobe. Table 4 shows the mental state classification results achieved with a reduced set of only 5 EEG electrodes compared with the 14 available EEG electrodes while employing all five EEG sub-bands or only the three low-frequency sub-bands (delta, theta, and alpha). Although using all 14 EEG channels and the five sub-bands resulted in the best performance with 96.8% accuracy, the accuracy loss while using only 5 EEG channels and three sub-bands was minor, with a degradation of only 3.2%. However, the benefit was reducing the area and power, since only 1202 neurons were required compared with 2776 neurons for the best case scenario.