Reservoir Computing Approaches to Microsleep Detection

Type of content
Journal Article
Thesis discipline
Degree name
Publisher
IOP Publishing
Journal Title
Journal ISSN
Volume Title
Language
eng
Date
2020
Authors
Ayyagari S
Jones R
Weddell, Stephen
Abstract

The detection of microsleeps in a wide range of professionals working in high-risk occupations is very important to workplace safety. A microsleep classifier is presented that employs a reservoir computing (RC) methodology. Specifically, echo state networks (ESN) are used to enhance previous benchmark performances on microsleep detection. A clustered design using a novel ESN-based leaky integrator is presented. The effectiveness of this design lies with the simplicity of using a fine-grained architecture, containing up to 8 neurons per cluster, to capture individualized state dynamics and achieve optimal performance. This is the first study to have implemented and evaluated EEG-based microsleep detection using RC models for the detection of microsleeps from the EEG. Microsleep state detection was achieved using a cascaded ESN classifier with leaky-integrator neurons employing 60 principal components from 544 power spectral features. This resulted in a leave-one-subject-out average detection in performance of Φ= 0.51 ± 0.07 (mean ± SE), AUC-ROC = 0.88 ± 0.03, and AUC-PR = 0.44 ± 0.09. Although performance of EEG-based microsleep detection systems is still considered modest, this refined method achieved a new benchmark in microsleep detection.

Description
Citation
Weddell S, Ayyagari S, Jones R (2020). Reservoir Computing Approaches to Microsleep Detection. Journal of Neural Engineering. 18(4). 046021-046021.
Keywords
EEG, classification, microsleep, reservoir computing
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Fields of Research::32 - Biomedical and clinical sciences::3209 - Neurosciences::320904 - Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience)
Fields of Research::40 - Engineering::4003 - Biomedical engineering::400309 - Neural engineering
Rights
All rights reserved unless otherwise stated