Preprint Concept Paper Version 1 Preserved in Portico This version is not peer-reviewed

Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States

Version 1 : Received: 19 June 2021 / Approved: 21 June 2021 / Online: 21 June 2021 (11:39:31 CEST)
Version 2 : Received: 29 June 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (12:42:59 CEST)

How to cite: Prakash, B.; Kumar Baboo, G.; Baths, V. Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States. Preprints 2021, 2021060509 (doi: 10.20944/preprints202106.0509.v1). Prakash, B.; Kumar Baboo, G.; Baths, V. Application of Graph Theory Features towards EEG Data Classification Models for Working Memory and The Emotional States. Preprints 2021, 2021060509 (doi: 10.20944/preprints202106.0509.v1).

Abstract

Functional Connectivity analysis using Electroencephalography signals is a common 2 practice. The EEG signals are converted to networks by transforming the signals into a correlation 3 matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regres 4 sion, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented 5 on the correlation matrix data to classify them either on their psychometric assessment or the 6 effect of therapy. The classifications based on RNN provided higher accuracy( 74-88%) compared 7 to the other three models( 50-78%). The use of a correlation matrix, instead of using individual 8 graph features provides an initial test of the data. When compared with the time-resolved correlation 9 matrix it provided 4-5% higher accuracy.

Keywords

EEG; Emotional States; Working Memory; Depression; Anxiety; Graph Theory; Classification; Machine Learning; Neural Networks.

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