Detection of microsleep states from the EEG: a comparison of feature reduction methods.
Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 7/8; pp. 1643 - 1658 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151585393&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151585393 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2021 vid: 59 iid: 7/8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151585393 151425982 151585393 NLM34275069 10.1007/s11517-021-02386-y NLM34275069 151585393 ppf: 1643 ppct: 15 formats: fmt: @attributes: type: P tig: atl: Detection of microsleep states from the EEG: a comparison of feature reduction methods. aug: au: Ayyagari, Sudhanshu S. D. P. Jones, Richard D. Weddell, Stephen J. affil: Department of Electrical and Computer Engineering, University of Canterbury, Christchurch, New Zealand sug: subj: Electroencephalography Algorithms Attention Discriminant Analysis Factor Analysis Clinical Assessment Tools ab: Microsleeps are brief lapses in consciousness with complete suspension of performance. They are the cause of fatal accidents in many transport sectors requiring sustained attention, especially driving. A microsleep-warning device, using wireless EEG electrodes, could be used to rouse a user from an imminent microsleep. High-dimensional datasets, especially in EEG-based classification, present challenges as there are often a large number of potentially useful features for detecting the phenomenon of interest. Thus, it is often important to reduce the dimension of the original data prior to training the classifier. In this study, linear dimensionality reduction methods-principal component analysis (PCA) and probabilistic PCA (PPCA)-were compared with eight non-linear dimensionality reduction methods (kernel PCA, classical multi-dimensional scaling, isometric mapping, nearest neighbour estimation, stochastic neighbourhood embedding, autoencoder, stochastic proximity embedding, and Laplacian eigenmaps) on previously collected behavioural and EEG data from eight healthy non-sleep-deprived volunteers performing a 1D-visuomotor tracking task for 1 h. The effectiveness of the feature reduction algorithms was evaluated by visual inspection of class separation on 3D scatterplots, by trustworthiness scores, and by microsleep detection performance on a stacked-generalisation-based linear discriminant analysis (LDA) system estimating the microsleep/responsive state at 1 Hz based on the reduced features. On trustworthiness, PPCA outperformed PCA, but PCA outperformed all of the non-linear techniques. The trustworthiness score for each feature reduction method also correlated strongly with microsleep-state detection performance, providing strong validation of the ability of trustworthiness to estimate the relative effectiveness of feature reduction approaches, in terms of predicting performance, and ability to do so independently of the gold standard. Graphical abstract Proposed microsleep detection system. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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