# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 6 Conclusions and Recommendations for Future Work  
The research paper proposes to perform a stress assessment in response to video clips using EEG. The arousal and valence values obtained from the DEAP dataset categorize participants in stressful and relaxed conditions. We extracted the power spectral density features (delta, theta, alpha, and beta) for stress assessment and applied four machine learning classifiers: NB, MLP, LR, and SMO. The performance analysis shows the proposed scheme's effectiveness. Furthermore, it is concluded that the proposed schemes provided promising results for stress assessment in response to the high arousal values videos. Alpha activity came out to be a statistically significant feature in the classification of stressful and relaxed conditions, which conforms to existing literature. For stress assessment, results obtained from overall feature extraction are satisfactory and worth considering for all metrics. The SMO classifier showed 95.65% accuracy for stressed and relaxed conditions in participants, which is better than all the classifiers used. There is a limitation in our study that using valence arousal-based labeling resulted in unbalanced classes in the DEAP dataset. This class imbalance can be removed by using techniques like SMOTE, GANs, and ADASYN in the future. In the future, we can extend our research on stress-induced responses to multimedia data on assessing high arousal by acquiring and analyzing the raw data through Android applications.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method,** & **No. of** & **Accuracy of** & **Classifier** & **Type of Stimuli** & **Sensor** \\
**Year** & **Participants** & **Results** & **Classifier** & **Type of Stimuli** & **Sensor** \\ \hline
[21], & 32 & 93.99\% & \
\begin{tabular}{c} Recurrent Attention \\ (DL algorithm) \\ \end{tabular} &
\begin{tabular}{c} Videos \\ (DEAP Dataset) \\ \end{tabular} & EEG \\
[20], & 32 & 71.76\% & K-NN & Videos (DEAP dataset) & EEG \\
2018 & 32 & 95.65\% & SMO & Videos (DEAP dataset) & EEG \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of the proposed scheme with State-of-art Methods.  
Personalized content recommendation systems on various media platforms can incorporate this model to track users' stress and emotional states in real-time while they watch videos. Furthermore, wearable technology can make use of this model to continuously monitor mental health, providing information on stressors and assisting people in managing their well-being. These potential applications show how versatile this research is in addressing modern challenges in mental health, multimedia consumption, and human-computer interaction. Examples of such applications include workplace productivity tools, gaming and virtual reality experiences, educational platforms, and therapeutic interventions.  
Conceptualization, S.M.U.S. and W.B.; methodology, S.M.U.S.; software, W.B and R.S.; validation, S.M.U.S., W.B., and M.A.; formal analysis, S.M.U.S.; investigation, W.B.; resources, M.A.; data curation, W.B. and R.S.; writing--original draft preparation, W.B., S.M.U.S., R.S., and M.A.; writing--review and editing, M.A. and R.S.; visualization, R.S.; supervision, S.M.U.S.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through Project number INST041.  
Not applicable.  
Data Availability Statement: Data is publicly available at [[https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html)]([https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html](https://www.eecs.qmul.ac.uk/mmv/datasets/deap/download.html)).  
The authors declare no conflicts of interest.