Academic Journal
Automated attention deficit classification system from multimodal physiological signals
| Title: | Automated attention deficit classification system from multimodal physiological signals |
|---|---|
| Authors: | Salankar, Nilima, Koundal, Deepika, Chakraborty, Chinmay, Garg, Lalit |
| Publisher Information: | Springer |
| Publication Year: | 2023 |
| Collection: | University of Malta: OAR@UM / L-Università ta' Malta |
| Subject Terms: | Electroencephalography -- Data processing, Attention -- Testing, Attention -- Physiological aspects, Signal processing -- Data processing, Neural networks (Computer science) |
| Description: | Lack of attention, if it could not be taken care of and persists for a long time then may lead to a severe issue. Analysis of Electroencephalogram (EEG) signals can effectively measure attention and its deficit. This paper proposed an efficient classification system to analyse and predict cognitive attention or its deficit with less computational power and adaptable in real-time. EEG signals have been split into six windows of varying time duration. Robust and computationally less expensive features hurst and power have been used for the designing of feature space. Objective of this proposed work is to provide robust methodology for classification of attentive and non-attentive category of subjects for real time screening. The robust classifier has been designed by multi-layer perceptron neural network and tuned with primary parameters and hyper-parameters using Adam optimisation. Gradient descent has been used for backpropagation. Hurst component of the signal has provided the self-similar characteristics. The features’ significance has been tested using the Wilcoxon signed-rank test. The experimental results have revealed that the proposed hybrid classification model could distinguish between an individual’s cases not being attentive and being attentive with accuracy of 88.04% at temporal lobe. ; peer-reviewed |
| Document Type: | article in journal/newspaper |
| Language: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/109436 |
| DOI: | 10.1007/s11042-022-12170-1 |
| Availability: | https://www.um.edu.mt/library/oar/handle/123456789/109436 https://doi.org/10.1007/s11042-022-12170-1 |
| Rights: | info:eu-repo/semantics/restrictedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Accession Number: | edsbas.81EC5BF0 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/109436# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated attention deficit classification system from multimodal physiological signals – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Salankar%2C+Nilima%22">Salankar, Nilima</searchLink><br /><searchLink fieldCode="AR" term="%22Koundal%2C+Deepika%22">Koundal, Deepika</searchLink><br /><searchLink fieldCode="AR" term="%22Chakraborty%2C+Chinmay%22">Chakraborty, Chinmay</searchLink><br /><searchLink fieldCode="AR" term="%22Garg%2C+Lalit%22">Garg, Lalit</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: Springer – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electroencephalography+--+Data+processing%22">Electroencephalography -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+--+Testing%22">Attention -- Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+--+Physiological+aspects%22">Attention -- Physiological aspects</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing+--+Data+processing%22">Signal processing -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+networks+%28Computer+science%29%22">Neural networks (Computer science)</searchLink> – Name: Abstract Label: Description Group: Ab Data: Lack of attention, if it could not be taken care of and persists for a long time then may lead to a severe issue. Analysis of Electroencephalogram (EEG) signals can effectively measure attention and its deficit. This paper proposed an efficient classification system to analyse and predict cognitive attention or its deficit with less computational power and adaptable in real-time. EEG signals have been split into six windows of varying time duration. Robust and computationally less expensive features hurst and power have been used for the designing of feature space. Objective of this proposed work is to provide robust methodology for classification of attentive and non-attentive category of subjects for real time screening. The robust classifier has been designed by multi-layer perceptron neural network and tuned with primary parameters and hyper-parameters using Adam optimisation. Gradient descent has been used for backpropagation. Hurst component of the signal has provided the self-similar characteristics. The features’ significance has been tested using the Wilcoxon signed-rank test. The experimental results have revealed that the proposed hybrid classification model could distinguish between an individual’s cases not being attentive and being attentive with accuracy of 88.04% at temporal lobe. ; peer-reviewed – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://www.um.edu.mt/library/oar/handle/123456789/109436 – Name: DOI Label: DOI Group: ID Data: 10.1007/s11042-022-12170-1 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/109436<br />https://doi.org/10.1007/s11042-022-12170-1 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/restrictedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. – Name: AN Label: Accession Number Group: ID Data: edsbas.81EC5BF0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-022-12170-1 Languages: – Text: English Subjects: – SubjectFull: Electroencephalography -- Data processing Type: general – SubjectFull: Attention -- Testing Type: general – SubjectFull: Attention -- Physiological aspects Type: general – SubjectFull: Signal processing -- Data processing Type: general – SubjectFull: Neural networks (Computer science) Type: general Titles: – TitleFull: Automated attention deficit classification system from multimodal physiological signals Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Salankar, Nilima – PersonEntity: Name: NameFull: Koundal, Deepika – PersonEntity: Name: NameFull: Chakraborty, Chinmay – PersonEntity: Name: NameFull: Garg, Lalit IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas |
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