# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 1 Introduction
[14] studied the EEG recordings of 50 participants using a 64 electrode array. The electrodes were placed above and beneath the right eye, and laterally with respect to the left and right eyes, to monitor vertical and horizontal eye movements, respectively. The EEG signals were pre-processed, with the raw signals being segmented into relevant intervals, and time-frequency representation was then implemented using the Stockwell transform [18]. Features were extracted from the time-frequency representation, after which certain time frames were discerned based on a set of stimuli between the time-frequency matrix representations of healthy and SZ patients. A high classification accuracy was yielded, with the best five distinct electrodes having a prediction accuracy ranging between 92.0% and 93.9%. The best electrode was found to be F2. [PERSON] et al. [19] acquired EEG recordings from participants using a 64 electrode system, and according to international standards, the patients were tasked with a memory working activity. Participants were required to press one of two response buttons, using either their right or left index finger, to indicate whether a particular letter was presented in the previous set. The signals were analyzed using the Brain Vision Analyser software and segmented via four stages of processing: pre-stimulus baseline, encoding, retention and retrieval. At each of the four stages of processing, time-frequency data (squared wavelet coefficients, binned and averaged according to correct versus incorrect response accuracy) were retrieved for the five frequency bands examined. Statistical analyses were conducted on spectral power measured at the Frontal, Central and Occipital locations. Featureselection was done using the wrapper method [20]. The 1-norm Support Vector Machine (SVM) classifier was utilized to classify correct and incorrect trials in data with the SVM Model 1, yielding a classification accuracy of 84%. The SVM Model 2 was implemented to classify normal versus SZ condition in correct trial data, achieving a classification accuracy of 87%. [PERSON] et al. [21] analyzed the EEG-event-related potentials(ERP) signals of participants who were involved in an auditory oddball task. The brain signals were recorded using Brain Vision equipment, in compliance with 10-20 international standards. After acquisition, the signals were pre-processed using EGGLAB, after which 16 time-domain features and four frequency-domain features were extracted per electrode, for each participant. Features were selected via linear discriminant analysis using J5, mutual information feature selection (MIFS), and double input symmetrical relevance. The Multilayer Perceptron (MLP) and SVM classifiers were employed for classification. High classification rates of 93.42% and 92.23% were achieved with the J5 MLP and J5 SVM classifiers, respectively. [PERSON] et al. [22] acquired EEG recordings from participants while they were at rest and engaged in a naming task. The Neuroscan SynAmps 32-channel amplifier was employed for the data acquirement. EEG signals at the resting phase were acquired prior to the task, while those from the task were extracted after each trial. In the resting phase, the segments were analyzed using a moving window method, after which Lempel-Ziv complexity (LZC) was computed per window. After normalization, the final LZC value was computed by calculating the average of the values obtained from the moving window method. A total of 80 EEG segments of \(2\times 10^{3}\) ms were evaluated, at task, and then averaged to obtain the final Multiscale LZC value. Higher complexity values were reported in right frontal regions of patients who were at rest. [PERSON] et al. [23] analyzed 2D time and frequency domain connectivity features and 1D intricate network features gauged from EEG signals. These features were then input to the Multi-domain connectome CNN model to obtain feature maps, which aided in the classification process. An accuracy of 93.06% was yielded.