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09-01-2022 | Research Article

Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals

Authors: Sara Bagherzadeh, Keivan Maghooli, Ahmad Shalbaf, Arash Maghsoudi

Published in: Cognitive Neurodynamics | Issue 5/2022

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Abstract

Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.

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Appendix
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Metadata
Title
Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals
Authors
Sara Bagherzadeh
Keivan Maghooli
Ahmad Shalbaf
Arash Maghsoudi
Publication date
09-01-2022
Publisher
Springer Netherlands
Published in
Cognitive Neurodynamics / Issue 5/2022
Print ISSN: 1871-4080
Electronic ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-021-09756-0

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