###### Abstract  
Schizophrenia is a chronic mental disorder that affects millions of people around the world. Neurologists commonly use EEG signals to distinguish schizophrenia patients from normal controls, but their manual analysis is tedious and time-consuming. This has motivated the need for automated methods based on machine learning. However, the methods based on hand-engineered features need human experts to decide which features should be extracted. Though deep learning has recently shown good results for schizophrenia detection, the existing deep models have high parameter complexity, making them prone to overfitting because the available data are limited. To overcome these limitations, we propose a method based on an ensemble-like approach and a lightweight one-dimensional convolutional neural network to discriminate schizophrenia patients from healthy controls. It splits an input EEG signal for analysis into smaller segments, where the same backbone model analyses each segment. In this way, it makes decisions after scanning an EEG signal of any length without increasing the complexity; i.e., it scales well with an EEG signal of any length. The model architecture is simple and involves a small number of parameters, making it easy to implement and train using a limited amount of data. Though the model is lightweight, enough trials are still needed to learn the discriminative features from available data. To tackle this issue, we introduce a simple data augmentation scheme. The proposed method achieved an accuracy of 99.88% on a public benchmark dataset; it outperformed the state-of-the-art methods. It will help neurologists in the rapid and accurate detection of schizophrenia patients. Keywords:schizophrenia; EEG classification; deep learning; convolutional neural network (CNN) +
Footnote †: journal: Medical  
Electroencephalograms (EEGs) are a common technique to measure the brain and its electrical activity. It is considered one of the most helpful brain imaging techniques due to the high resolution of its spatiotemporal data via a direct representation of the brain dynamics [8]. In addition, it provides better discrimination between the brain images of healthy people and those with mental diseases [9]. Moreover, it has a low cost compared to other brain imaging techniques. EEG signals are recorded by placing several channels along the scalp. The number of channels usually varies according to the features needed to be extracted. One of the most common systems for placing the channels is the 10-20-placement system [10]. Visual analysis of EEG signals by neurologists is laborious and subjective. Different machine-learning algorithms have been proposed to overcome this problem and assist the neurologists. Traditional algorithms are based on hand-engineering techniques, including feature extraction, feature selection, and classification. However, there are some limitations to using these algorithms to classify EEG signals of schizophrenia. One of their limitations is that the feature extraction process is done without learning the characteristics of the data. In addition, the number of selected features might be high, which leads to the curse of the dimensionality problem. These limitations usually affect classification accuracy and make it unacceptable for diagnosis systems. The limitations of traditional machine learning techniques and the outstanding performance of deep learning in various applications motivated the use of deep learning for schizophrenia detection, and many deep learning-based techniques have been proposed [11; 12; 13; 14; 15; 16]. The performance of these techniques is good; they are prone to overfitting because they have highly complex architectures involving a large number of learnable parameters, but the available data for schizophrenia detection are limited.