Paper Info
Title
A Hybrid-Domain Deep Learning-Based Bci For Discriminating Hand Motion Planning From Eeg Sources
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%.
Year
DOI
Venue
2021
10.1142/S0129065721500386
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Keywords
DocType
Volume
Deep learning, brain-computer interface, electroencephalography, beamforming, wavelet transform, feature fusion
Journal
31
Issue
ISSN
Citations 
09
0129-0657
1
PageRank 
References 
Authors
0.36
0
4
Name
Order
Citations
PageRank
Cosimo Ieracitano1182.75
F. C. Morabito2385.27
Amir Hussain367267.84
Nadia Mammone413619.69