A Hybrid-Domain Deep Learni/ng-Based BCI For Discriminating Hand Motion Planning From EEG Sources

Ieracitano, Cosimo; Morabito, Francesco Carlo; Hussain, Amir; Mammone, Nadia

Abstract

In this paper, a hybrid-domain deep learning (DL)-based neural system is proposed to decode hand movement preparation phases from electroencephalographic (EEG) recordings. The system exploits information extracted from the temporal-domain and time-frequency-domain, as part of a hybrid strategy, to discriminate the temporal windows (i.e. EEG epochs) preceding hand sub-movements (open/close) and the resting state. To this end, for each EEG epoch, the associated cortical source signals in the motor cortex and the corresponding time-frequency (TF) maps are estimated via beamforming and Continuous Wavelet Transform (CWT), respectively. Two Convolutional Neural Networks (CNNs) are designed: specifically, the first CNN is trained over a dataset of temporal (T) data (i.e. EEG sources), and is referred to as T-CNN; the second CNN is trained over a dataset of TF data (i.e. TF-maps of EEG sources), and is referred to as TF-CNN. Two sets of features denoted as T-features and TF-features, extracted from T-CNN and TF-CNN, respectively, are concatenated in a single features vector (denoted as TTF-features vector) which is used as input to a standard multi-layer perceptron for classification purposes. Experimental results show a significant performance improvement of our proposed hybrid-domain DL approach as compared to temporal-only and time-frequency-only-based benchmark approaches, achieving an average accuracy of 76.21±3.77%.

• Journal Article Type: Article
• Acceptance Date: Jun 23, 2021
• Online Publication Date: Aug 11, 2021
• Publication Date: 2021-09
• Deposit Date: Nov 8, 2021
• Journal: International Journal of Neural Systems
• Print ISSN: 0129-0657
• Electronic ISSN: 1793-6462
• Publisher: World Scientific Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 9
• Article Number: 2150038
• DOI: https://doi.org/10.1142/s0129065721500386
• Keywords: Deep learning, brain–computer interface, electroencephalography, beamforming, wavelet transform, feature fusion
• Public URL: http://researchrepository.napier.ac.uk/Output/2804674