Schizo-Net: A <i>novel</i> Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices-Reference-Cited by-同舟云学术

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

Published:2023 Issue: Volume:31 Page:464-473
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Grover Nitin¹^ORCID,Chharia Aviral²^ORCID,Upadhyay Rahul¹^ORCID,Longo Luca³^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

2. Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

3. Artificial Intelligence and Cognitive Load Research Laboratory, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin 7, Ireland

Funder

Artificial Intelligence and Cognitive Load Research Laboratory, Applied Intelligence Research Centre, Technological University Dublin, Ireland

Thapar Institute of Engineering and Technology Seed Money Grant for the Project “Analysis of Electroencephalogram Signals for implementation of P300-based Brain Computer Interface”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering,General Neuroscience,Internal Medicine,Rehabilitation

Link

http://xplorestaging.ieee.org/ielx7/7333/10031624/10023506.pdf?arnumber=10023506

Reference78 articles.

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4. Selection of relevant features for EEG signal classification of schizophrenic patients

5. Deep learning methods in electroencephalography;kotowski;Learning and Analytics in Intelligent Systems,2020

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