## 5 Discussion  
The main purpose of this paper is to propose an efficient emotion recognition algorithm, which is reliable, simple, and easy to apply in the practical environment but remains relatively high performance. The method development of the previous studies requires the decomposition techniques for the generation of various sub-bands, which are theta, alpha, beta, gamma, and delta corresponding to different frequency bands, from the preprocessed EEG signals. Clearly, 4 sub-bands and 32 channels are the most widely used for feature extraction, which results in a huge input feature space. Therefore, the existing works consider a small number of the original features to reduce the feature dimension and complexity for the method development such as 9, 10, 4, 6, 4, 1, 13, 1 features of [4,14,15,18,20,22,23,29], respectively. However, numerous features, which are omitted certainly, are able to contribute significantly to the final emotion detection performance of the proposed algorithm. Consequently, a large set of 42 original features is investigated in our research for better selection of the informative feature subsets. Another significant characteristic is that the combined activities of different human brain areas represent various fundamental brain functions including human emotions. Obviously, the interaction between brain regions related to emotions is shown by the channel correlation in a specific group. As a result, the EEG recorded from the individual brain areas using electrodes contains significant information on human emotions. In other words, the selected electrodes represent informative brain regions, which generate high-quality EEG signals for emotion recognition. Therefore, the identification of effective human brain regions plays a vital role in the noble EEG generation for the extraction of high-quality features. In this work, the dual GA-based channel and feature selection are employed to select the most relevant channels and features in terms of improvement of the final emotion detection. Moreover, the wise-subject 5-fold CV-based statistic manner is combined with the GA method resulting in reinforcement of the performance reliability of the proposed algorithm and avoidance of overfitting problem. The cerebral cortex is divided into frontal, temporal, parietal, central, and occipital lobes as shown in Figure 3. It is noteworthy that motor and sensitive functions are of the central lobe, which makes it part of the frontal and parietal cortex [56]. The simulation results of channel selection prove that the left and frontal cortex are productive for high-quality EEG generation related to emotion recognition. Although selected lobes are located over the entire cortex, most of them are on the left cortex as shown in Figure 3. Moreover, higher execution including emotional regulation, planning, reasoning, and problem-solving is implemented in the frontal brain area. Therefore, the EEG signals generated from this brain region using 4 channels contain a high proportion of relevant information related to human emotions. There are 3 and 2 EEG channels selected from the central and parietal cortex, which is responsible for motion and sensory information. Visual information and language processing are produced by the occipital and temporal cortex corresponding to OZ and T7 channels selected by the GA method. Clearly, all brain lobes are related to human emotions but only a few specific areas, where electrodes are located selectively for the EEG collection, are highly significant for emotion detection. A total of 11 channels corresponding to different brain regions are addressed as the most relevant channels for feature extraction. In addition, the GA combined with wise-subject 5-fold CV is adopted for the feature selection in this research.