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    Fusion of Multivariate EEG Signals for Schizophrenia Detection using CNN and Machine Learning Techniques

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    Author
    Hassan, Fatima
    Hussain, Syed Fawad
    Qaisar, Saeed
    Subject
    Schizophrenia; Electroencephalography (EEG); Convolutional neural network (CNN); Logistic regression
    Date
    2023-04
    
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    Abstract
    Schizophrenia is a severe mental disorder that has adverse effects on the behavior of an individual such as disorganized speech and delusions. Electroencephalography (EEG) signals are widely used for its identification as they are non-invasive and have high temporal resolution. EEG signals may be captured using wearable devices but transmission of complete data from all channels is both battery and data consuming. Several studies on Schizophrenia have either used all channels or relied on sophisticated feature extraction algorithms to find the most relevant EEG channels for further processing. That too, however, needs data from all channels beforehand to identify the most relevant features. In this study, a publicly available multi-channel EEG signals dataset from the institute of Psychiatry and Neurology in Warsaw, Poland is studied for an automated identification of Schizophrenia using only a subset of data from selected channels. To achieve this, we device a channel selection mechanism based on a rigorous performance analysis of the Convolutional Neural Network (CNN) while considering the individual EEG channels at different brain regions. The selected channels are combined, and we use a fusion of CNN and different machine learning (ML) classifiers to train the classification model. Our experiments show that a combination of three channels namely, T4, T3, and Cz achieves 90% and 98% accuracies on subject-based and non-subject based testing, respectively, using a hybridization of CNN and logistic regression (LR).
    Department
    Electrical and Computer Engineering
    Publisher
    Elsevier
    Sponsor
    Effat University
    Journal title
    Information Fusion
    DOI
    https://doi.org/10.1016/j.inffus.2022.12.019
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1016/j.inffus.2022.12.019
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