## 2 Literature Review
### Deep Learning-Based Techniques  
Many deep learning-based techniques have been proposed, such as taking raw EEG trials as input or converting them into 2D images. [PERSON] et al. [22] developed two CNN models consisting of 11 layers to classify the raw EEG signals. [PERSON] et al. [23] introduced two LSTM architectures and two channels (FP1 and FP2) for schizophrenia detection. In another study, [PERSON] et al. [24] also used LSTM for schizophrenia classification. They used 6790 features extracted using three non-linear methods, Katz fractal dimension, approximate entropy, and variance, which are fed to the LSTM model.  
Another approach is combining the CNN and LSTM models for schizophrenia detection. [PERSON] et al. [25] compared different traditional and deep learning techniques. In their method, the EEG signal is segmented into trials of 25 s, which are normalized using the z-score and L2 methods. The best accuracy was achieved with 1D-CNN-LSTM involving ReLU activation. [PERSON] et al. [11] also introduced a method based on the CNN and LSTM models. [PERSON] et al. [12] built an LSTM model to classify EEG signals as healthy control or schizophrenia.  
[PERSON] et al. [13] utilized the RLNDiP method to extract discriminate features from the time domain (TD) and time-frequency domain (TFD) of the EEG signals. After that, the Kruskal-Wallis test is used to select the dominant features that are fed to the ANN model.  
Some studies first converted an EEG signal trial into a 2D image and then employed different 2D-CNN models designed for Image Processing and Computer Vision tasks. [PERSON] et al. [14] converted EEG signal trials into 2D images using spectrograms and fed them to 2D CNN models. For training CNN models, they used two meta-learning methodologies, Model-Agnostic Meta-Learning (MAML) and prototypical networks, to overcome the issue of the limited dataset. [PERSON] et al. [15] transformed EEG trials into 2D images using Continuous Wavelet Transform (CWT) and employed a pre-trained VGG-16 model for classification. [PERSON] et al. [16] utilized discrete short-time Fourier transform (DSTFT) to convert EEG signal trials into images and then extracted features by a Siamese neural network (SNN), which was followed by an XGBoost classifier.  
[PERSON] et al. [26] adopted Recurrence Plot (RP) and Gramian Angular Field (GAF) methods to transform the EEG signal into an image that was fed to VGGNet. A combination of functional connectivity theory and deep learning model was employed by [PERSON] et al. [27] to classify the EEG signal. The frequency bands of the EEG signal were extracted using continuous wavelet transform (CWT). After that, the cross-mutual information (CMI) method was employed to convert data into a functional connectivity matrix, which was then fed to the 3D-CNN classifier.  
A close look at the existing methods reveals that, firstly, the complex deep models were used with relatively small datasets, which is an indicator of the overfitting problem; the methods in [11; 14; 15; 22; 24; 25] achieved accuracies of 98.07%, 99%, 99.25%, 99.9%, 96.83%, and 99.5%, respectively. Moreover, some studies [11; 12; 13; 14; 16; 21; 22; 24; 27] used EEG trials of long temporal lengths in a range between 20 to 60 s, which requires more computation and time. Also, in [23], the authors adopted only two channels, FP1 and FP2, without providing enough information on why they chose them. [PERSON] et al. [15] adopted very deep, complex, and pre-trained VGG-16. The LSTM classifier was used extensively to detect schizophrenia using 1D EEG signals. Thus, it is necessary to investigate the design of lightweight 1D CNN models. Therefore, we proposed a lightweight CNN method that takes a raw EEG signal (1D) as input to detect schizophrenia.