## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sleep Stage** & **Characteristic Frequency** \\ \hline W & Alpha (8–12 Hz) \\ \hline N1 & Theta (4–8 Hz) \\ \hline N2 & Spindle (12–15 Hz) \\ \hline N3 & Delta (0.5–4 Hz) \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline
**REM** & Alpha (8–12 Hz) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EEG signal characteristic frequency ranges for every sleep stage [5]. Figure 1: Classification of sleep stages [2]. MI can help in identifying the most discriminative EEG features related to sleep stages, thereby enhancing the model's capacity to discriminative between them. In addition, EEG signals often have high dimensionality due to the large number of electrodes and time points. ANOVA and MI will help to reduce the dimensionally by selecting the most effective features, which can lead to a more efficient and accurate CNN model. Selecting the most relevant features using these FS techniques can help prevent overfitting and improve the generalization ability of the proposed CNN model on EEG signals. Finally, FS techniques can reveal insights into the underlying patterns and features of EEG signals related to different sleep stages, advancing our understanding of brain activity through sleep. The main contribution of deep learning-based models for EEG (electroencephalogram) signal classification, coupled with ANOVA (analysis of variance) and mutual information feature selection methods, lies in their ability to effectively extract relevant features from EEG data and improve classification performance. Combining deep learning models with ANOVA and mutual information feature selection techniques leverages the strengths of both approaches. Deep learning models provide the capacity to learn complex representations directly from raw EEG data, while ANOVA and mutual information feature selection help identify relevant features and reduce the dimensionality of the feature space, thereby enhancing classification accuracy and interpretability. Overall, this integrated approach can lead to more robust and effective EEG signal classification systems, with applications in various domains such as brain-computer interfaces, healthcare, and neuroscience research. This research is presented in different sections. A brief survey of related studies is provided in Section 2. The proposed architecture is shown in Section 3. Section 4 discusses the specifics of the suggested methodology and algorithmic reasoning. A description of the result analysis is provided in Section 5. Finally, Section 6 discusses our study's concluding ideas. Research Questions  
To Assist Scientists Investigating EEG Analysis Using FS and DL:  
1. With what deep learning model should researchers start with EEG analysis? Furthermore, improving the feature selection methods that work well can improve sleep stage classification accuracy. By providing researchers with the necessary resources and information, our work helps them effectively navigate the subject of deep learning and establish a strong foundation in it. 2. Which major EEG datasets are the best places for academics to begin their EEG-DL research? This paper offers a perceptive summary of recent developments, highlighting well-known datasets and the DL techniques that go along with them. By responding to these research questions, researchers may remain ahead of the curve in their area and uncover interesting possibilities for their studies by staying up to speed with cutting-edge research directions.