Time Series Classification of Electroencephalography Data

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Rushbrooke, Aiden, Tsigarides, Jordan ORCID: https://orcid.org/0000-0001-9893-8002, Sami, Saber and Bagnall, Anthony (2023) Time Series Classification of Electroencephalography Data. In: Advances in Computational Intelligence. IWANN 2023. Advances in Computational Intelligence, 14134 . Springer, pp. 601-613. ISBN 9783031430848

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Abstract

Electroencephalography (EEG) is a non-invasive technique used to record the electrical activity of the brain using electrodes placed on the scalp. EEG data is commonly used for classification problems. However, many of the current classification techniques are dataset specific and cannot be applied to EEG data problems as a whole. We propose the use of multivariate time series classification (MTSC) algorithms as an alternative. Our experiments show comparable accuracy to results from standard approaches on EEG datasets on the UCR time series classification archive without needing to perform any dataset-specific feature selection. We also demonstrate MTSC on a new problem, classifying those with the medical condition Fibromyalgia Syndrome (FMS) against those without. We utilise a short-time Fast-Fourier transform method to extract each individual EEG frequency band, finding that the theta and alpha bands may contain discriminatory data between those with FMS compared to those without.

Item Type: Book Section
Uncontrolled Keywords: eeg,fibromyalgia,time series classification,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614
Faculty \ School: Faculty of Science
Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Mental Health
Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Medicine and Health Sciences > Research Centres > Lifespan Health
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Depositing User: LivePure Connector
Date Deposited: 25 Oct 2023 02:32
Last Modified: 31 Oct 2023 02:48
URI: https://ueaeprints.uea.ac.uk/id/eprint/93465
DOI: 10.1007/978-3-031-43085-5_48

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