Research Repository

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). pp. 142-153. ISSN 1751-9675

IET_Paper (2).pdf - Accepted Version

Download (3MB) | Preview


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
Uncontrolled Keywords: electroencephalography; signal classification; medical signal processing; brain-computer interfaces; infrared spectroscopy; recurrent neural nets; convolutional neural nets; simultaneous EEG-fNIRS signal classification; deep recurrent-convolutional neural network; spatial features; temporal features; human brain; deep neural network model; adjacent signals; complex correlations; near-infrared spectroscopy; EEG signals; traditional BCI systems; brain-computer interface
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 May 2020 10:50
Last Modified: 23 Sep 2022 19:37

Actions (login required)

View Item View Item