Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals. Issue 3 (1st May 2020)

• Record Type: Journal Article
• Authors: Ghonchi, Hamidreza; Fateh, Mansoor; Abolghasemi, Vahid; Ferdowsi, Saideh; Rezvani, Mohsen
• Is Part Of: IET signal processing. Volume 14:Issue 3(2020)
• Page Start: 142
• Page End: 153
• Publication Date: 2020-05-01
• Subjects: 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; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2019.0297
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : 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%.