Channel Selection and Creation Algorithms for Electroencephalography Classification with HIVE-COTE

Rushbrooke, Aiden, Middlehurst, Matthew, Sami, Saber and Bagnall, Tony (2025) Channel Selection and Creation Algorithms for Electroencephalography Classification with HIVE-COTE. In: UNSPECIFIED.

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Abstract

Electroencephalography (EEG) is a crucial tool across neuroscience domains, including medical diagnostics, psychological research, and brain-computer-interfacing (BCI), due to its non-invasiveness, high temporal resolution, and cost-effectiveness. EEG data classification—the process of assigning predefined class labels to segments of EEG recordings based on patterns learned from training data—is challenging due to EEG’s high dimensionality, variability, and individual-specific differences. Recent research has shown that the time series classification algorithm HIVE-COTE v2.0 (HC2) is particularly effective at EEG classification, but that it is also orders of magnitude slower than algorithms from the EEG literature. We investigate ways of improving the run time of HC2 through channel selection and creation. We demonstrate that we can achieve accuracy that is not significantly different to full HC2 with up to 3 times faster runtime.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	eeg,machine learning,time-series
Faculty \ School:	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 Medicine and Health Sciences > Research Centres > Mental Health and Social Care (fka Lifespan Health)
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	05 Jun 2026 15:13
Last Modified:	05 Jun 2026 15:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/103305
DOI:	10.1007/978-3-032-08462-0_26

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