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Publication
From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach
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Advisor(s)
Abstract(s)
Deep learning techniques have been applied to electroencephalogram (EEG) signals,
with promising applications in the field of psychiatry. Schizophrenia is one of the most
disabling neuropsychiatric disorders, often characterized by the presence of auditory
hallucinations. Auditory processing impairments have been studied using EEG-derived
event-related potentials and have been associated with clinical symptoms and cognitive
dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP
components, such as the auditory N100, some have been proposed as biomarkers
of schizophrenia. In this paper, we examine altered patterns in electrical brain activity
during auditory processing and their potential to discriminate schizophrenia and healthy
subjects. Using deep convolutional neural networks, we propose an architecture to
perform the classification based on multi-channels auditory-related EEG single-trials,
recorded during a passive listening task. We analyzed the effect of the number of
electrodes used, as well as the laterality and distribution of the electrical activity over
the scalp. Results show that the proposed model is able to classify schizophrenia
and healthy subjects with an average accuracy of 78% using only 5 midline channels
(Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning
methods in the study of impaired auditory processing in schizophrenia with implications
for diagnosis. The proposed design can provide a base model for future developments
in schizophrenia research.
Description
Keywords
Auditory processing Convolutional neural network Deep learning EEG Schizophrenia
Pedagogical Context
Citation
Barros, C., Roach, B., Ford, J. M., Pinheiro, A. P., & Silva, C. A. (2022). From sound perception to automatic detection of schizophrenia: an EEG-based deep learning approach. Frontiers in Psychiatry, 12, 813460. https://doi.org/10.3389/fpsyt.2021.813460
Publisher
Frontiers Media