# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 6 Results and Discussion  
For model evaluation, we discuss the results of the proposed models themselves and compare their performance with existing methods and previous studies that used SEED.  
First, we assessed the predictive performance of our proposed models (CNN, LSTM, and the hybrid model) using commonly used ML metrics: accuracy, precision, and recall (Table 2). We also used the confusion matrix and the learning curves of accuracy, training loss of 80%, and validation loss of 20% over epochs.  
Accuracy is the percentage of the total number of correct predictions that actually occurred. Precision is the percentage of properly identified positive cases. Recall is the percentage of real positive cases that were properly identified [40].  
We achieved the highest performance in classification choice through our proposed LSTM model after resampling the dataset with 96% accuracy and 95% for both precision and recall. The hybrid model came in second with 91% accuracy, followed by CNN with 88% accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Classifier** & **Accuracy** & **Precision** & **Recall** \\ \hline \multirow{3}{*}{Without Resampling} & CNN & 75\% & 68\% & 54\% \\  & LSTM & 71\% & 83\% & 21\% \\  & Hybrid & 76\% & 72\% & 54\% \\ \hline \multirow{3}{*}{With Resampling} & CNN & 88\% & 83\% & 93\% \\  & LSTM & 96\% & 95\% & 95\% \\ \cline{1-1}  & Hybrid & 91\% & 95\% & 86\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance measurements of the proposed models.  
Figure 6: Hybrid model architecture.  
In our study, we considered all three evaluation measures because the ML model needs to truly identify and not miss the like states as positive emotions and not misclassify the actual dislike states as like states. The confusion matrix is used to understand what type of mistakes (like or dislike) our proposed models made during the training and testing phases. Figure 7 shows the number of actual choice states versus the predicted choice states for our proposed models, CNN and LSTM.  
The error rate (misclassification rate) of the LSTM model was 0.4. It predicted two wrong like states (false positive) and two wrong dislike states (false negative). In contrast, the error rate of the CNN model increased to 0.11. It predicted eight false like states (false positive) and three false dislike states (false negative).  
To ensure that our proposed model has no over- or under-fitting problems, learning curves are used to display the change in training accuracy versus validation accuracy over epochs and the training loss versus validation loss over epochs. Figure 8 depicts the CNN model's accuracy and loss during the training and validation phases. As shown in the plots, the model converged slowly over epochs, and the convergence stopped after epoch 10.  
Figure 8: Proposed CNN model accuracy and loss.  
Figure 7: Confusion matrices of the proposed CNN (**b**) and LSTM (**a**).  
Figure 9 depicts the LSTM model's accuracy and loss during the training and validation phases. As can be observed from the graph, the model converged over epochs faster than the CNN model, and it is normally fitted with the highest accuracy and the least loss.