About: EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network
Creator	Ammar, Achraf Bouaziz, Bassem Chaari, Lotfi Boudaya, Amal Chaabene, Siwar Hökelmann, Anita
Source	PMC
abstract	Hypo-vigilance detection is becoming an important active research areas in the biomedical signal processing field. For this purpose, electroencephalogram (EEG) is one of the most common modalities in drowsiness and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a and awakeness detection. In this context, we propose a new EEG classification method for detecting fatigue state. Our method makes use of a Convolutional Neural Network (CNN) architecture. We define an experimental protocol using the Emotiv EPOC+ headset. After that, we evaluate our proposed method on a recorded and annotated dataset. The reported results demonstrate high detection accuracy (93%) and indicate that the proposed method is an efficient alternative for hypo-vigilance detection as compared with other methods.
has issue date	2020-05-31(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51517-1_6
has license	cc-by
sha1sum (hex)	cced5a8644a7304ffc5c5e4ea23dfc5b48a30d9e
schema:url	https://doi.org/10.1007/978-3-030-51517-1_6
resource representing a document's title	EEG-Based Hypo-vigilance Detection Using Convolutional Neural Network
has PubMed Central identifier	PMC7313303
schema:publication	The Impact of Digital Technologies on Public Health in Developed and Developing Countries
resource representing a document's body	covid:cced5a8644a7304ffc5c5e4ea23dfc5b48a30d9e#body_text
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