## 3 Materials and Methods
### Materials Preprocessing  
The dataset is preprocessed with different techniques for further feature extraction as follows:  
(a) Preprocessing  
We use the DEAP dataset for this work, which was preprocessed by [39] as follows:  
1. The data was downsampled to 128 Hz.
2. EOG artefacts were removed.
3. A bandpass frequency filter from 4-45 Hz was applied.
4. The data was averaged to the common reference.
5. The EEG channels were reordered so that they all follow the Geneva order as above.
6. The data was segmented into 60-s trials and a 3-s pre-trial baseline removed.  
After preprocessed by [39], the individual EEG signals have a length of 63 s including the first 3 s of the pretrial baseline, which are then removed to correct the stimulus changes of EEG signals. As a result, 32 (subjects) \(\times\) 40 (videos) \(\times\) 32 (channels) \(\times\) 7860 (data points) as considered as the preprocessed EEG signals using for our work. It is noteworthy that we do not implement a filter to denoise EOG and EMG because this technique has already completed by [39].  
(b) Labelling  
Two dimensions of emotions are selected namely valence and arousal for further classifications in the DEAP dataset. Each dimension ranges from 1 to 9 in which a threshold of 5 is employed. Binary classifications are for two scenarios using the above threshold, which are high/low valence and high/low arousal. As a result, four emotions namely Joy, Calm, Disgust, and Sadness are identified by High Valence High Arousal (HVHA), High Valence Low Arousal (HVLA), High Arousal Low Valence (HALV), and Low Valence Low Arousal (LVLA), respectively.  
(c) Signal band construction  
The DWT is an effective method known as a nonredundant sampled continuous wavelet transform (CWT) technique [40], which generates a set of wavelet coefficients of the discrete time series. Then, an orthogonal set of basic function is constructed by the use of the above wavelet coefficients, which ensures the nonredundant representation. By breaking down a signal into components at different frequencies and scales, DWT is particularly well-fitted in application for the non-stationary EEG signals [13], which makes DWT as a widely applied method for time-frequency analysis in the fields of neuroscience and biomedical engineering. The DWT of S(t) is defined as [41]:  
\[W_{\varnothing}(i_{0},k)=\frac{1}{\sqrt{N}}\sum_{t}S(t)\varnothing_{i_{0},k}(t) \tag{1}\]  
and  
\[W_{\psi}(i,k)=\frac{1}{\sqrt{N}}\sum_{k}S(t)\psi_{i,k}(t), \tag{2}\]where, \(\varnothing(t)\) and \(\psi(t)\) are wavelet function and scaling function, respectively. \(N\) is power of 2. \(i\) is the scale parameter with \(i=0,1,2,\ldots,I-1\), and \(k\) is the shift parameter with \(k=0,1,2,\ldots,2^{i}-1\). \(S(t)\) is reconstructed by  
\[S(t)=\frac{1}{N}\sum_{k}W_{\varnothing}(i_{0},k)\mathcal{O}_{i_{0},k}(t)+ \frac{1}{\sqrt{N}}\sum_{i=i_{0}}^{I-1}\sum_{k}W_{\psi}(i,k)\psi_{\mathrm{i,k}}( \mathrm{t}). \tag{3}\]  
There are two components constructed by the DWT namely the low-pass and high-pass filters, which produce the approximate and detail signals using a scaling and a wavelet functions as shown in (1) and (2), respectively. The signal decomposition process is implemented recursively through various levels using repeatedly the high- and low-pass filters in which the detail signal is kept in current level while the approximate signal is continuously decomposed by the above filters in following level. We use four sub-bands constructed from preprocessed EEG signals using the DWT method with the fourth Daubechies mother wavelet function (db4). Here, the preprocessed EEG signals are decomposed into different sub-bands corresponding to frequency ranges such as theta of [4; 5; 6; 7; 8] Hz, alpha of [8; 9; 10; 11; 12; 13; 14; 15; 16] Hz, beta of [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32] Hz, and gamma of [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45] Hz. Figure 2 shows an example of different band signals.