Classification of EEG-based effective brain connectivity in schizophrenia using deep neural networks

Chun-Ren Phang, Chee-Ming Ting, S. Balqis Samdin, Hernando Ombao

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

25 Citations (Scopus)


Disrupted functional connectivity patterns have been increasingly used as features in pattern recognition algorithms to discriminate neuropsychiatric patients from healthy subjects. Deep neural networks (DNNs) were employed to fMRI functional network classification only very recently and its application to EEG-based connectome is largely unexplored. We propose a DNN with deep belief network (DBN) architecture for automated classification of schizophrenia (SZ) based on EEG effective connectivity. We used vector-autoregression-based directed connectivity (DC), graph-theoretical complex network (CN) measures and combination of both as input features. On a large resting-state EEG dataset, we found a significant decrease in synchronization of neural oscillations measured by partial directed coherence, and a reduced network integration in terms of weighted degrees and transitivity in SZ compared to healthy controls. The proposed DNN-DBN significantly outperforms three other traditional classifiers, due to its inherent capability as feature extractor to learn hierarchical representations from the aberrant brain network structure. Combined DC-CN features gives further improvement over the raw DC and CN features alone, achieving remarkable classification accuracy of 95% for the theta and beta bands.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering
EditorsMichel Maharbiz, Cynthia Chestek
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538679210
ISBN (Print)9781538679227
Publication statusPublished - 2019
Externally publishedYes
EventInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019 - San Francisco, United States of America
Duration: 20 Mar 201923 Mar 2019
Conference number: 9th

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554


ConferenceInternational IEEE Engineering-in-Medicine-and-Biology-Society (EMBS) Conference on Neural Engineering (NER) 2019
Abbreviated titleNER 2019
Country/TerritoryUnited States of America
CitySan Francisco
Internet address

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