# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 7 Conclusions  
Neuroscientists use EEG-based BCI to measure brain activity and find the neural correlates of decision-making processes, which involve both cognitive and emotional factors. By employing AI and DL, it becomes feasible to make more accurate predictions regarding the outcomes of decisions and individuals' behavior and choices [28].  
This research has demonstrated the potential of utilizing DL models and EEG signals to build an accurate individual choice prediction system. The proposed LSTM model achieved a high accuracy rate of 96%, which outperformed classical classifiers and the results of previous studies that used SEED.  
The use of fewer channels for feature extraction reduced computation time and increased the practicality of the model for EEG datasets recorded using devices with fewer  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Class Labels** & **\# of Selected** & **Extracted** & **\# of** & **Classifier** & **Accuracy** \\ \hline \multirow{8}{*}{[8]} & \multirow{8}{*}{Positive, negative, neutral} & \multirow{8}{*}{62} & PSD, DE, DASM, & \multirow{8}{*}{—} & DBN & 86.08\% \\ \cline{4-7}  & & & RASM, DCAU & & SVM & 83.99\% \\ \cline{4-7}  & & & RASM, DCAU & & LR & 82.70\% \\ \cline{4-7}  & & & & KNN & 72.60\% \\ \cline{4-7}  & & 12 (FT7, FT8, T7, T8, C5, & & & \\  & & C6, TP7, TP 8, C75, CP6, & & & \\  & & P7, P8) & & & & \\ \hline
[27] & Positive, negative, neutral & 62 & DWT-BODF & & & SVM & 93.8\% \\ \hline
[28] & Positive, negative & 62 & DySampEns & 1798 & SVM & 85.11\%. \\ \hline
[29] & Positive, negative & 8 (AF3, AF4, F3, F4, F7, F8, & & WT and & \\  & & T7, T8) & Sample entropy & — & LSTM & 90.12\% \\ \hline \multirow{4}{*}{Our Work} & \multirow{4}{*}{Positive, negative} & 12 (FP1, FPZ, FP2, AF3, & & DE, choice indices, & \\  & & AF4, FZ, F3, F4, F8, F7, & & PSD, average & 39,767 & Hybrid & 91\% \\ \cline{4-7}  & & F5, F6) & power & & XGBoost & 86\% \\ \cline{4-7}  & & & & CNN & 88\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of previous methodologies used in the benchmark SEED.  
channels. Overall, this study provides valuable insights into the development of accurate prediction models for individual choices and behavior using EEG signals.  
For future work, we plan to investigate the role of feature selection of the extracted features to improve the performance of detecting the choice states in the SEED dataset.  
[PERSON] researched the literature, analyzed and interpreted the data, and drafted the manuscript. [PERSON] and [PERSON] implemented the experiment. [PERSON] and A.A.-N. designed and supervised the analysis, reviewed and edited the manuscript, and contributed to the discussion. All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-RG23079).  
Not applicable.  
The data presented in this study are available upon request from the Center for Brain-like Computing and Machine Intelligence Laboratory. \"[[https://bcmi.sjtu.edu.cn/home/seed/index.html](https://bcmi.sjtu.edu.cn/home/seed/index.html)]([https://bcmi.sjtu.edu.cn/home/seed/index.html](https://bcmi.sjtu.edu.cn/home/seed/index.html))\" (accessed on 20 January 2023).  
The authors would like to thank the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) for funding and supporting this research.  
The authors declare no conflict of interest.