High-wearable EEG-based distraction detection in motor rehabilitation

Abstract A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions f...

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Autores principales:	Andrea Apicella, Pasquale Arpaia, Mirco Frosolone, Nicola Moccaldi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34b25583fa064205bf6171817308ecf5
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spelling	oai:doaj.org-article:34b25583fa064205bf6171817308ecf52021-12-02T13:20:12ZHigh-wearable EEG-based distraction detection in motor rehabilitation10.1038/s41598-021-84447-82045-2322https://doaj.org/article/34b25583fa064205bf6171817308ecf52021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84447-8https://doaj.org/toc/2045-2322Abstract A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness.Andrea ApicellaPasquale ArpaiaMirco FrosoloneNicola MoccaldiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrea Apicella Pasquale Arpaia Mirco Frosolone Nicola Moccaldi High-wearable EEG-based distraction detection in motor rehabilitation
description	Abstract A method for EEG-based distraction detection during motor-rehabilitation tasks is proposed. A wireless cap guarantees very high wearability with dry electrodes and a low number of channels. Experimental validation is performed on a dataset from 17 volunteers. Different feature extractions from spatial, temporal, and frequency domain and classification strategies were evaluated. The performances of five supervised classifiers in discriminating between attention on pure movement and with distractors were compared. A k-Nearest Neighbors classifier achieved an accuracy of 92.8 ± 1.6%. In this last case, the feature extraction is based on a custom 12 pass-band Filter-Bank (FB) and the Common Spatial Pattern (CSP) algorithm. In particular, the mean Recall of classification (percentage of true positive in distraction detection) is higher than 92% and allows the therapist or an automated system to know when to stimulate the patient’s attention for enhancing the therapy effectiveness.
format	article
author	Andrea Apicella Pasquale Arpaia Mirco Frosolone Nicola Moccaldi
author_facet	Andrea Apicella Pasquale Arpaia Mirco Frosolone Nicola Moccaldi
author_sort	Andrea Apicella
title	High-wearable EEG-based distraction detection in motor rehabilitation
title_short	High-wearable EEG-based distraction detection in motor rehabilitation
title_full	High-wearable EEG-based distraction detection in motor rehabilitation
title_fullStr	High-wearable EEG-based distraction detection in motor rehabilitation
title_full_unstemmed	High-wearable EEG-based distraction detection in motor rehabilitation
title_sort	high-wearable eeg-based distraction detection in motor rehabilitation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/34b25583fa064205bf6171817308ecf5
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High-wearable EEG-based distraction detection in motor rehabilitation

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