Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop br...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Giovanni Vecchiato
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	hybrid systems action prediction driving EEG EMG EOG Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/2af9f386bd7f4a76aafa5cdf74fa86ff
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:2af9f386bd7f4a76aafa5cdf74fa86ff
record_format	dspace
spelling	oai:doaj.org-article:2af9f386bd7f4a76aafa5cdf74fa86ff2021-12-01T14:25:35ZHybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios2673-619510.3389/fnrgo.2021.784827https://doaj.org/article/2af9f386bd7f4a76aafa5cdf74fa86ff2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnrgo.2021.784827/fullhttps://doaj.org/toc/2673-6195The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.Giovanni VecchiatoFrontiers Media S.A.articlehybrid systemsaction predictiondrivingEEGEMGEOGNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neuroergonomics, Vol 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid systems action prediction driving EEG EMG EOG Neurology. Diseases of the nervous system RC346-429
spellingShingle	hybrid systems action prediction driving EEG EMG EOG Neurology. Diseases of the nervous system RC346-429 Giovanni Vecchiato Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
description	The complexity of concurrent cerebral processes underlying driving makes such human behavior one of the most studied real-world activities in neuroergonomics. Several attempts have been made to decode, both offline and online, cerebral activity during car driving with the ultimate goal to develop brain-based systems for assistive devices. Electroencephalography (EEG) is the cornerstone of these studies providing the highest temporal resolution to track those cerebral processes underlying overt behavior. Particularly when investigating real-world scenarios as driving, EEG is constrained by factors such as robustness, comfortability, and high data variability affecting the decoding performance. Hence, additional peripheral signals can be combined with EEG for increasing replicability and the overall performance of the brain-based action decoder. In this regard, hybrid systems have been proposed for the detection of braking and steering actions in driving scenarios to improve the predictive power of the single neurophysiological measurement. These recent results represent a proof of concept of the level of technological maturity. They may pave the way for increasing the predictive power of peripheral signals, such as electroculogram (EOG) and electromyography (EMG), collected in real-world scenarios when informed by EEG measurements, even if collected only offline in standard laboratory settings. The promising usability of such hybrid systems should be further investigated in other domains of neuroergonomics.
format	article
author	Giovanni Vecchiato
author_facet	Giovanni Vecchiato
author_sort	Giovanni Vecchiato
title	Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
title_short	Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
title_full	Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
title_fullStr	Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
title_full_unstemmed	Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios
title_sort	hybrid systems to boost eeg-based real-time action decoding in car driving scenarios
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/2af9f386bd7f4a76aafa5cdf74fa86ff
work_keys_str_mv	AT giovannivecchiato hybridsystemstoboosteegbasedrealtimeactiondecodingincardrivingscenarios
_version_	1718405047549689856

Hybrid Systems to Boost EEG-Based Real-Time Action Decoding in Car Driving Scenarios

Ejemplares similares