## 1 Introduction
### Deep-Learning Methods  
Owing to the availability of large datasets, researchers have used neural networks to find inexpensive and suitable solutions. This is why they adopt a deep-learning architecture. These innovations have led to an increase in deep-learning applications over the past two decades. Deep learning has demonstrated excellent performance in processing images [29], videos [30], speech [31], and text [32]. Because neural networks update their variables automatically, they do not require much prior knowledge of the dataset, which is very difficult to interpret with large datasets, even for experts. In recent years, owing to the collection and availability of large-scale EEG data, deep learning has been used to decode and classify EEG patterns, which typically have low signal-to-noise ratios [33]. A previous study [34] reported lower accuracy after examining various EEG features and neural network designs. [PERSON] et al. [34] tried three different combinations of calculated features. However, it was found that even a mixture of PCE, CSA, and PSD features could not correct SAE deficiencies for this dataset. [PERSON] and [PERSON] [35] compared the accuracy between SAE and several DBNs and found that DBNs with three restricted Boltzmann devices had different RBMs than SAE and DBN. Correlation layers were used in five of these studies. Two of these five architectures were combinations in which a CNN was sent to the LSTM modules of an RNN. However, none of these methods reach sufficient accuracy. The differences in accuracy among the three standard CNN studies were likely due to differences in the input formulation. [PERSON] and [PERSON] [36] used signal values as inputs to a neural network, while [36; 37] transformed the data into Fourier feature maps and 3D neural networks with excellent accuracy. These CNN architectures consist of two convolution layers, each with one or two dense layers. Only the MLPNN architecture applied to this dataset [38] achieved excellent accuracy, comparable to that of a standard CNN that uses signal values. However, the input formulation may be the main factor affecting the difference in accuracy. Whereas CNN Deep uses signal quantities that require significant preprocessing, MLPNN requires extensive effort to preprocess the input with PSD features and forward asymmetric channel selection. First, the architecture includes two layers of LSTM [39], with one dense layer, which connects to an RNN. Networks without convolution layers are created. For the higher cases of this group, the deep-learning regression architecture without convolution layers, built to outperform other architectures, is similar to iterative and motion-aspect architectures. From investigations using the same dataset, DBN, CNN, and RNN architectures were determined to be the most suitable and effective. The selection of suitable formulas made the input suitable for the CNN. The signal values were the best with the RNN, while computed properties, especially PSD properties, performed better with the DBN. Machine learning provides and applies models as effective solutions [40; 41; 42; 43] in most cases because although neuroscientists provide knowledge and procedures for processing and diagnosis, signals vary over time, which can be addressed by machine-learning approaches. Many classifiers, such as neural networks [44; 45; 46; 47; 48; 49], support vector machines (SVMs) [50; 51], and hidden Markov models [52; 53], have been used to classify EEG signals. Mental activity patterns based on potentials were detected by updating neural networks using a propagation approach after EEG classification was used [54]. Researchers have used deep learning based on neural networks (CNN, RNN, etc.) to solve complex problems, and they have performed very well in these fields [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69]. In [69; 70], the authors proposed a new feature that preserved the spatial, spectral, and temporal structures of the EEG.