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Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals

Ghonchi, Hamidreza and Fateh, Mansoor and Abolghasemi, Vahid and Ferdowsi, Saideh and Rezvani, Mohsen (2020) 'Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals.' IET Signal Processing, 14 (3). 142 - 153. ISSN 1751-9675

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Brain–computer interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on electroencephalogram (EEG) signals only. Recently, researchers have used a combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with functional near-infrared spectroscopy (fNIRS) has achieved favourable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates the spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%.

Item Type: Article
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 04 May 2020 10:50
Last Modified: 25 Aug 2020 13:15

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