Feature Selection Using F-statistic Values for EEG Signal Analysis

Genchang Peng, Mehrdad Nourani, Jay Harvey, Hina Dave

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

Abstract

Electroencephalography (EEG) is a highly complex and non-stationary signal that reflects the cortical electric activity. Feature selection and analysis of EEG for various purposes, such as epileptic seizure detection, are highly in demand. This paper presents an approach to enhance classification performance by selecting discriminative features from a combined feature set consisting of frequency domain and entropy based features. For each EEG channel, nine different features are extracted, including six sub-band spectral powers and three entropy values (sample, permutation and spectral entropy). Features are then ranked across all channels using F-statistic values and selected for SVM classification. Experimentation using CHB-MIT dataset shows that our method achieves average sensitivity, specificity and F-1 score of 92.63%, 99.72% and 91.21%, respectively.

Original languageEnglish (US)
Title of host publication42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publicationEnabling Innovative Technologies for Global Healthcare, EMBC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5963-5966
Number of pages4
ISBN (Electronic)9781728119908
DOIs
StatePublished - Jul 2020
Externally publishedYes
Event42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
Duration: Jul 20 2020Jul 24 2020

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2020-July
ISSN (Print)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period7/20/207/24/20

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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