# Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning
## 5 Experiment Implementation
### Dataset Description  
One of our main challenges in this research was the lack of EEG emotional benchmark datasets. However, there are some publicly available EEG emotional benchmark datasets used in neuromarketing research, such as the DEAP dataset [34], the neuromarketing dataset [35], and SEED [36].  
In this paper, we used SEED to train the model of the proposed system. EEG datasets were provided by the brain-like computing and machine intelligence laboratory, Shanghai Jiao Tong University. The SEED dataset has been used to conduct multiple studies and has been proven to be well-suited for testing new algorithms [8; 27; 28; 29]. These studies have demonstrated the effectiveness of various ML techniques, including deep learning approaches, in classifying emotional states using the SEED dataset. Table 1 summarizes some information about SEED.  
SEED contains EEG signal recordings of 15 Chinese participants (7 males and 8 females) that were collected with 62 channels. Their emotions during the recording were triggered using 15 Chinese film clips containing positive, neutral, and negative emotions. The video clips (stimuli) were selected with specific criteria: (1) each video clip includes only one  
\begin{table}
\begin{tabular}{l l} \hline \hline Affective Model & Valence dimensional emotion model \\ \hline Participants & 15 Chinese participants (7 males and 8 females) \\ \hline Stimuli Type & 15 Chinese film clips each elicits a single target emotion: positive (1), neutral (0), or negative (\(-1\)) \\ \hline Stimuli Duration & Each film clip lasts about 4 min \\ \hline Stimuli & No consecutive trials (film clips) triggering the same \\ Arrangement & emotional type \\ \hline Trials & 675 trials: 3 sessions for each subject; each experiment session \\  & contained 15 trials (film clips) \\ \hline  & \(\bullet\) & Participants completed the experiment in three sessions, \\  & \(\bullet\) & one week apart to ensure the stability of reading signals. \\  & \(\bullet\) & Each session had 15 video clips presented in an \\ Experiment & \(\bullet\) & emotion-alternating order. \\ Protocol & \(\bullet\) & Each video clip had a 5 s hint at the start, followed by a \\  & & 4 min video clip presentation with a 45 s \\  & \(\bullet\) & self-assessment questionnaire. \\  & \(\bullet\) & A 15 s rest followed afterward. \\ \hline EEG Device & 62-channel ESI NeuroScan System \\ \hline  & Active AgCl electrodes: FP1, FP2, FP2, AF3, AF4, F7, F5, F3, F1, \\  & FZ, F2, F4, F6, F8, FT7, CFC5, F3, FC1, FCZ, FC2, FC4, FC6, FT8, \\ Electrodes & 77, C5, C3, C1, CZ, CZ, C4, C6, T8, TP7, CP5, CP3, CP1, CPZ, CP2, \\  & CP4, CP6, T9R, F7, T5, P3, P1, PZ, P2, P4, P6, P8, P07, PO5, PO3, \\  & POZ, PO4, PO6, POS, CBI, O1, OZ, O2, CB2 \\ \hline  & \(\bullet\) & Downsampled to 200 Hz \\ Preprocessing EEG Signals & \(\bullet\) & EMG and EOG were removed manually \\  & \(\bullet\) & Bandpass frequency filter between 0.5 Hz and 75 Hz \\  & \(\bullet\) & EEG signal was divided into 1s long data segments \\  & without overlap \\ \hline \hline \end{tabular}
\end{table}
Table 1: SEED Description.  
targeted emotion; (2) the video content is clear and can be understood without descriptions; and (3) each experiment has an appropriate duration to avoid subjects experiencing fatigue.  
In SEED, each participant completed the experiment through three different sessions with a time interval of about one week to ensure the stability of reading signals. Each session contained fifteen different video clips, and their presentations were ordered in such a way that two clips did not trigger the same emotion consecutively. There was a 5 s hint at the start before each video clip, followed by the 4 min video clip presentation and a 45 s self-assessment questionnaire to report the immediate emotional reactions to each video clip. A 15 s rest is provided afterward. Each video clip was linked into one emotion: positive, negative, and neutral labeled as 1, \(-\)1, and 0, respectively.