Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia

In our recent study, scalograms of EEG signals were used to identify potential biomarkers in schizophrenia heritability. This study presented evidence for the validation of a Convolutional Neural Network (CNN) model using transfer learning for scalp EEGs of patients and controls during the performance of a speeded sensorimotor task and a working memory task. It was shown that the theta and alpha frequency bands of the EEG signals are significantly relevant to the CNN classification decision and predict the first-degree relatives indicating potential heritable functional deviances. The proposed methodology results in important advancements for the identification of biomarkers in schizophrenia heritability.

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Korda, A. I., Ventouras, E., Asvestas, P., Toumaian, M., Matsopoulos, G. K., Smyrnis, N. (2022). Convolutional neural network propagation on electroencephalographic scalograms for detection of schizophrenia. Clinical Neurophysiology, 139, 90-105. https://doi.org/10.1016/j.clinph.2022.04.010

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