# Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration
## 3 Multi-Modal System EEG with Facial Expressions--Proposed Model
### EEG Electrode and Frequency Band Selection  
The red, green, and blue layers are used to represent different colors on a digital display screen, and they are utilized to store the measured topographies that are gathered from the gathered database. In other words, each visible color is represented digitally by three values, and each one is between 0 and 255 per pixel. As a result, each topography can be thought of as a three-layered matrix, where R, G, and B are the colors that correspond to each layer. Each image in this study is considered to be a square of points with 64 \(\times\) 64 points in each color layer, for a total of three squares. Each pixel in the image has three values or three points representing the amount of red, green, and blue colors intensities. According to Ref. [38], the objective function is selected to be the total of the three RGB layers of the absolute difference value between the averages of a set of EEG topographies collected for two distinct classes of emotions. For a given emotion axis (valence in this case) a total of N and M EEG topographic images are obtained for high and low valence, respectively, without sacrificing generality. The average of the pixel values computed independently for each layer or color C (R, G, B) determines the average EEG topographic heat map for each emotional level. The absolute difference between the high and low classes of each emotion's average EEG topography is then computed [38]. The formula that is produced and maximized using simplicial homology global optimization (SHGO) [38] is  
\[\bar{\Delta}_{C}[64\times 64]=\left|\sum_{i=1}^{N}\frac{High[E][C]_{i}}{N}- \sum_{i=1}^{M}\frac{Low[E][C]_{i}}{M}\right| \tag{1}\]  
\(C\) represents one of the three colors (R, G, B), resulting in three absolute differences in the average topography. The overall sum of the matrix, composed of each \(\bar{\Delta}_{R}\), \(\bar{\Delta}_{G}\), \(\bar{\Delta}_{B}\), entails adding 64 by 64 by 3 for (R, G, and B), totaling 49,152 elements. These elements signify the variance in average pixel values between high and low categories on the valence or arousal scale. Three absolute variations in the average topography are produced in this case, where \(C\) represents one of the three (R, G, and B) colors and \(N\) and \(M\) are the numbers of high and low emotional axis topographies, respectively. The difference between the average value for each pixel between the high and low categories of the valence scale is represented by 64 \(\times\) 64 \(\times\) 3 for (R, G, and B), or 49,152 elements or pixels, which make up the total sum of the matrix sum of each \(\Delta\bar{R}\), \(\Delta\bar{G}\), and \(\Delta\bar{B}\). \[ObjectiveFunction=\bar{\Delta}_{R}+\bar{\Delta}_{G}+\bar{\Delta}_{B} \tag{2}\]  
As a result, SHGO approaches have shown encouraging results in terms of improving EEG emotion recognition channels. This method maximizes the difference between high and low levels of valence by optimizing the target frequency band of the measured signal, the number of EEG channels, and their placements. The objective function was maximized by using the SHGO optimization algorithm. The SHGO algorithm, like any optimization method, tests the consequences of changing each input parameter in order to investigate several alternatives. There are fifteen parameters to take into account in this scenario: the frequency range (0 = theta, 1 = alpha, 2 = beta, and 3 = gamma) and the fourteen EEG channels that can be active or deactivated (multiplied by 0 or 1). Thus, the total number of combinations is \(4^{1}\times 2^{14}=2^{16}\). The power spectral density, which is derived from all 14 of the activated channels in one of the four frequency bands, is essentially what creates the topography heat map. In the topography, all inactive channels are represented by zero values (multiplied by zero). The resulting objective function to be maximized depends on maximizing the absolute difference between the two topographies representing the two opposite emotional axis levels pixel by pixel.