Personalized Feature Selection for Wearable EEG Monitoring Platform

Genchang Peng, Mehrdad Nourani, Jay Harvey, Hina Dave

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

Abstract

Electroencephalography (EEG) signal monitoring can be applied for many purposes, such as epileptic seizure detection. To design a reliable, wearable EEG monitoring platform for seizure detection in daily use, this paper presents a two-step approach to select a small subset of discriminative features from a few number of channels. In the first step, linear discriminant analysis (LDA) is applied to choose informative channels which have highly-ranked LDA criterion values. Then in the second step, the least absolute shrinkage and selection operator (LASSO) method is adopted to incrementally add features into selection subset. To determine the best number of channels and features for each subject, a personalization technique is utilized by evaluating the classification result of different feature subsets based on support vector machine (SVM) classifier. Experimentation on CHB-MIT database shows that on average, the proposed method selects approximately 3 channels and 7 features, and yields F-1 score of 81% based on SVM evaluation.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages380-386
Number of pages7
ISBN (Electronic)9781728195742
DOIs
StatePublished - Oct 2020
Event20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States
Duration: Oct 26 2020Oct 28 2020

Publication series

NameProceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
CountryUnited States
CityVirtual, Cincinnati
Period10/26/2010/28/20

Keywords

  • Feature selection
  • least absolute shrinkage and selection operator
  • linear discriminant analysis
  • personalization.

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Molecular Biology
  • Artificial Intelligence
  • Computer Science Applications
  • Biomedical Engineering
  • Modeling and Simulation
  • Health Informatics

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