# Enhanced Classification of Video-Evoked Stress Response Using Power Spectral Density Features
## 5 Comparison and Discussion of Results  
EEG is widely used for stress assessment along with the PSS-10 Questionnaire [48; 49]. However, studies for stress assessment in response to multimedia are limited in the literature as evident by Table 7. The primary part of multimedia is obtained from the internet, and in the last few years, there has been an increase in the use of online social networks [50]. These social networks allow people to comment, share, and observe multimedia content such as images, audio, and video clips. That may induce emotions such as happiness, distress, sadness, depression, satisfaction, calm, delight, and excitement. These emotions induce stress in people, leading to mental disorders and causing many other diseases [51]. In [21], a new method is developed to improve the accuracy of emotional stress assessment. For that, a combination of a 3D (three-dimensional) neural network and attention method is developed to make the 3D convolutional gated self-attention neural network. Then, the experiment is performed on benchmark datasets, including the DEAP, VRE, and EDESC datasets, to competently perceive emotional stress.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **Kappa** & **Fm** \\ \hline NB & 88.04\% & 0.91 & 0.92 & 0.69 & 0.91 \\ MLP & 94.56\% & 0.94 & 0.98 & 0.85 & 0.96 \\ SMO & 95.65\% & 0.95 & 0.98 & 0.88 & 0.97 \\ LR & 92.39\% & 0.96 & 0.92 & 0.81 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different classifiers for stressed and relaxed groups based on the EEG recording while watching the videos.  
Figure 5: Confusion matrices for NB, MLP, SMO, and LR for two-class stress classification based on the EEG recordings while watching the videos.  
They use the 3D feature vector by applying the 3D convolution block (3 DConvB) to the 3D cuboid representation (Cb) of the EEG data in a specific frequency band (b). This 3D cuboid representation consists of the power spectral density (PSD) values (Xb) for each electrode in that frequency band. The results of the experiments were worthwhile. An accuracy of 93.99% for self-gated is obtained for the DEAP dataset. In [20] Emotional stress detection is made by performing the genetic algorithm for feature selection using the DEAP dataset. Emotional stress is detected by developing a new feature selection algorithm that is called the genetic algorithm for the DEAP dataset. This algorithm selects the feature that enhances the performance of the classifiers. The number of features extracted is 192. They used the K-NN classifier and the K-NN classification algorithm, which showed an accuracy of 71.76%. However, in our study, we extracted 4 features for 32 channels, so the features for our research are 128 (4 * 32). DEAP dataset video sessions are used as multimedia stimuli. NB, SMO, LR, and MLP classifiers are used to classify stressed and relaxed participants. The results showed the highest accuracy of 95.65% for the SMO classifier. If we compare the proposed study with [20,21] data from only 32 subjects, it was acquired and analyzed, but we obtained results with improved accuracy.  
In [21], the same dataset is used that we have used in our study. They used the deep learning recurrent algorithm for feature extraction and the 3D neural network model. By this, an accuracy of 93.9% is obtained. However, we have extracted spectral density features for the delta, theta, alpha, and beta bands. We have used machine learning algorithms for classification, and an accuracy of 95.65% was obtained for the SMO classifier. In [20], a genetic algorithm is developed and is employed on the same dataset as ours. However, an accuracy of 71.76% is obtained. The studies [20,21] both focus on emotional stress using the DEAP dataset. Therefore, these studies are closely related to our proposed work.