# A Two-Step Framework to Recognize Emotion Using the Combinations of Adjacent Frequency Bands of EEG
## 4 Materials and Experiments  
### Database Introduction and Preprocessing  
To assess the effect of the emotion recognition framework, we conduct the experiments on two widely used public EEG databases, and Table 1 shows the particulars of the original and preprocessed data in both databases. The DEAP [39] database contains the EEG signals with 32 channels and a 512 Hz sampling rate from 32 subjects (50% female, mean age 26.9 years) watching 40 videos with a length of 63 s (a one-minute music video plus a three-second baseline). Each subject has to perform a trial in which they have to self-assess their arousal, valence, liking, and dominance in each music video, and the self-assessment score is the label of the current recorded EEG signal. In our experiment, the preprocessing processes of the DEAP database are conducted by downsampling the EEG signals to 128 Hz, eliminating the artifacts, and filtering the signal into different frequency bands. Then, the PSD of each frequency band is extracted with a window size of three seconds and no overlap. Additionally, in the initial moment of each music video, there is a three-second baseline signal. Thus, each processed segment \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Database** & **Data Content** & **Data Shape** & **Data Description** \\ \hline \multirow{4}{*}{DEAP} & Raw data & \(40\times 32\times\) & Video \(\times\) channel \(\times\) \\  & (\(128\times 63\)) & (sample rate \(\times\) time) \\ \cline{2-3}  & Preprocessed data & \(W_{1}\times 128\) & sample number \(\times\) channel \(\times\) band) \\ \cline{2-3}  & Preprocessed labels & \(W_{1}\times 2\) & sample number \(\times\) (A, V) \\ \cline{2-3}  & Number of dataset & 32 & trial \\ \hline \multirow{4}{*}{SEED IV} & Raw data & \(15\times 62\times(200\times n_{i})\) & video \(\times\) channel \(\times\) (sample rate \(\times\) time) \\ \cline{2-3}  & Preprocessed data & \(W_{2}\times 310\) & sample number \(\times\) (channel \(\times\) band) \\ \cline{1-1} \cline{2-3}  & Preprocessed labels & \(W_{1}\times 4\) & sample number \(\times\) (H, S, F and N) \\ \cline{1-1} \cline{2-3}  & Number of dataset & 45 & trial \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of the pre-processed database. (Arousal and valence are represented by A and V, respectively; happy, sad, fear, and neutral are represented by H, S, F and N, respectively.) Figure 4: The training process of ensemble learning (each rectangle in the training feature set represents a concatenated vector). has twenty-one samples, the first one is a baseline and the last twenty are useful samples with specific emotions. The PSD feature is obtained by the deviation of PSD from useful samples and the baseline. Afterward, the PSD features of one trial with 32 channels and four frequency bands are processed into the shape of \(W_{1}\times 128\), where \(W_{1}=800\). In addition, we only use the assessment scores of arousal and valence as the labels for each trial. The SEED IV [40] database records the EEG signals of 15 subjects (7 males and 8 females) through inviting them to watch emotionally stimulating videos. Each subject has to watch 24 stimulating videos with four emotional states (happy, sad, fear, and neutral) for about \(n_{i}\) seconds on three different days. This means that the SEED IV database has three sessions, each with 15 trials. For each participant, the EEG signals are recorded with 62 channels and a 1000 Hz sampling rate, and the labels of the recorded EEG signals are the emotion states of the corresponding video. In our experiment, we use the preprocessed dataset \"de_movingAve\" from the SEED IV database. The preprocessing includes downsampling the EEG signals to 200 Hz, removing the noise, eliminating the artifacts, and extracting the differential entropy (DE) feature with a time window of four seconds without overlap. Through the transformation provided by [PERSON] et al.