Time Series Classification of Electroencephalography Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence. IWANN 2023
Subtitle of host publicationLecture Notes in Computer Science
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
Number of pages13
ISBN (Print)9783031430848
Publication statusPublished - 30 Sep 2023

Publication series

NameAdvances in Computational Intelligence
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • EEG
  • Fibromyalgia
  • Time series classification

Cite this