# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 5 Conclusions  
This article suggested utilizing a biologically inspired SNN-based architecture for feature extraction in the frequency domain. We proposed an original approach for classifying mental states and situation awareness based on EEG signals and a biologically inspired SNN using an array of unique SCTN-based resonators and a classification SNN. Narrowing the frequency bands to three sub-bands achieved area savings with comparable classification results to the best case scenario. Moreover, using only 5 out of the 14 EEG electrodes resulted in sufficient accuracies of 94.9% and 93.6% with five and three EEG sub-bands, respectively, while significantly reducing the number of required neurons by about 50%. The results demonstrate a strong correlation between the SCTN-based bio-inspired features and the FFT-extracted features. This suggests that the proposed method could be effectively applied to diverse applications. Simulations showed that our approach outperformed previous related works, demonstrating 96.8% accuracy in classifying the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Focus** & **Unfocused** & **Drowsing** \\ \hline
**Focus** & **98.8\%** & 0.8\% & 0.4\% \\ \hline
**Unfocused** & 0.9\% & **95.2\%** & 3.9\% \\ \hline
**Drowsy** & 0.8\% & 2.8\% & **96.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **FPR** \\ \hline
**Focus** & 96.8\% & 98.3\% & 99.4\% & 0.9\% \\ \hline
**Unfocused** & 96.8\% & 96.4\% & 97.4\% & 1.8\% \\ \hline
**Drowsy** & 96.8\% & 95.7\% & 98.2\% & 2.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation in terms of the four metrics: accuracy, sensitivity, specificity, and false positive rate (FPR). three mental states (focused, unfocused, and drowsy). Moreover, we achieved 93.6% accuracy with only five EEG electrodes compared with 91.7% with 14 electrodes in [24]. The proposed SNN-based classifier had a particularly good generalization ability, therefore enabling using individual training paradigms (i.e., for a specific participant) and then successfully applying the trained model to other participants. The proposed approach was evaluated using a limited number of subjects, and therefore, we believe generalizability for a large number of subjects and checking more awareness states would better demonstrate the power of the suggested method. This study described the potential usage of spiking neural networks for EEG signal analysis. Future works may expand the proposed approach to apply a neuromorphic SCTN-based classification network to further EEG-based applications like early detection of epileptic seizures, among others. Conceptualization, M.B. and S.G.; methodology, M.B. and S.G.; software, Y.H. ; validation, Y.H. and M.B. ; formal analysis, Y.H. and M.B. ; investigation, Y.H. and M.B. ; writing--original draft preparation, M.B., S.G. and Y.B.-S.; writing--review and editing, M.B., S.G. and Y.B.-S.; visualization, Y.H. ; supervision, S.G. and Y.B.-S.; project administration, S.G. and Y.B.-S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not Applicable. The source code and dataset can be found in the following GitHub Repository: [[https://github.com/NeuromorphicLabBGU/Situational-Awareness-Classification](https://github.com/NeuromorphicLabBGU/Situational-Awareness-Classification)]([https://github.com/NeuromorphicLabBGU/Situational-Awareness-Classification](https://github.com/NeuromorphicLabBGU/Situational-Awareness-Classification)). The authors declare no conflicts of interest.