{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:49:38Z","timestamp":1778201378765,"version":"3.51.4"},"reference-count":54,"publisher":"World Scientific Pub Co Pte Lt","issue":"09","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:p> 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 [Formula: see text]%. <\/jats:p>","DOI":"10.1142\/s0129065721500386","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T06:12:47Z","timestamp":1628662367000},"page":"2150038","source":"Crossref","is-referenced-by-count":60,"title":["A Hybrid-Domain Deep Learning-Based BCI For Discriminating Hand Motion Planning From EEG Sources"],"prefix":"10.1142","volume":"31","author":[{"given":"Cosimo","family":"Ieracitano","sequence":"first","affiliation":[{"name":"DICEAM, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, Reggio Calabria, 89124, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesco Carlo","family":"Morabito","sequence":"additional","affiliation":[{"name":"DICEAM, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, Reggio Calabria, 89124, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[{"name":"School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, Scotland, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia","family":"Mammone","sequence":"additional","affiliation":[{"name":"DICEAM, University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, Reggio Calabria, 89124, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"S0129065721500386BIB001","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aab2f2"},{"key":"S0129065721500386BIB002","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/bs.pbr.2016.04.017","volume":"228","author":"M\u00fcller-Putz G. 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