@inproceedings{fdab5d62a5b14c42ac206a65cca9389e,
title = "Evaluation of eeg connectivity network measures based features in schizophrenia classification",
abstract = "In this paper an architecture for the classification of Schizophrenia using EEG-based brain connectivity is proposed. Functional and effective networks were constructed from the EEG using a variety of connectivity measures and with graph theory metrics complex network features were extracted. Several classification algorithms were used for the evaluation of the architecture. Promising results were observed when using connectivity measures that also capture directionality properties of the network. The best classification accuracy was 82.36% and was achieved by Random Forest classifier with Direct Transfer Function as a connectivity measure.",
keywords = "classification, connectivity, EEG, schizophrenia",
author = "Vasiliki Bougou and Iosif Mporas and Pascal Schirmer and Todor Ganchev",
year = "2019",
month = nov,
doi = "10.1109/BIA48344.2019.8967453",
language = "English",
series = "Proceedings of the International Conference on "Biomedical Innovations and Applications", BIA 2019",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
editor = "Valentina Markova and Todor Ganchev",
booktitle = "Proceedings of the International Conference on {"}Biomedical Innovations and Applications{"}, BIA 2019",
note = "2019 International Conference on Biomedical Innovations and Applications, BIA 2019 ; Conference date: 08-11-2019 Through 09-11-2019",
}