## 1 Introduction
Obviously, channel and feature selection plays a crucial role in the optimization of the intelligent model. However, existing works have not considered properly the channel selection for the model optimization. Indeed, the entire channels are commonly used for feature extraction in most of the existing works [2,4,5,10]. Furthermore, a small amount of the original features is considered for the feature extraction from various channels and signal sub-bands, which results in a huge feature space used as the input of different models. It is noteworthy that the identification of the most relevant feature subset extracted from different sub-bands is an especially time-consuming procedure. Additionally, signal decomposition plays a crucial role in EEG analysis. The study in [12] presents the Empirical Wavelet Transform (EWT) as a potential method. This method decomposes signals into their frequency components, enabling detailed analysis by constructing a customized wavelet basis. Another approach, as demonstrated in [13], highlights the effectiveness of decomposing signals using Discrete Wavelet Transform (DWT). DWT effectively decomposes signals into sub-bands, including theta, delta, beta, and gamma, which are valuable for extracting key features in EEG analysis applications. Indeed, DWT is a crucial method for capturing both slow-varying low-frequency components and rapidly changing high-frequency components, making it well-suited for analyzing discrete EEG signals. Motivated by the above analysis, we propose an emotion detection algorithm using Machine Learning (ML) and Deep Learning (DL) techniques. The proposed method uses Discrete Wavelet Transform (DWT) to decompose the preprocessed EEG signals into different sub-bands of frequencies, which are then adopted for the extraction of numerously original features. Moreover, the Genetic Algorithm (GA)-based exhaustive search method in combination with the wise-subject cross-validation (CV) and an ML model as the fitness function is implemented to address the optimal channels and feature subsets. Finally, the proposed algorithm is validated for its detection performance by the wise-subject CV method on a separate dataset to improve the reliability of the applications in practical environments. The main contributions of this work are as follows:  
* Investigation of expanded input features extracted from various sub-bands constructed from preprocessed EEG signals, which is effective in addressing the most relevant feature subsets in terms of detection performance improvement. * Quality improvement of the final feature subsets by the application of dual GA procedures, in which the first GA is used to select the most productive channels using the total input features, and the second GA identifies the most informative features extracted from the selected channels. * Proposal of a reliable algorithm for emotion detection, which is based on performance result comparisons of various models using a wise-subject CV-based statistical method. This paper is organized as follows: Section 2 presents a literature review. Section 3 describes the materials and methods used in the research. Section 4 provides the simulation results, followed by the discussion in Section 5. Finally, Section 6 summarizes the key findings and concludes the paper.