Time series classification for EEG eye state identification based on incremental attribute learning

Ting Wang, Sheng Uei Guan, Ka Lok Man, T. O. Ting

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

13 Citations (Scopus)

Abstract

Electroencephalography (EEG) eye state classification is important and useful to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). Experimental results show that, with proper feature extraction and feature ordering, IAL can not only cope with time series classification problems efficiently, but also exhibit better classification performance in terms of classification error rates in comparison with other approaches.

Original languageEnglish
Title of host publicationProceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014
PublisherIEEE Computer Society
Pages158-161
Number of pages4
ISBN (Print)9781479952779
DOIs
Publication statusPublished - 2014
Event2nd International Symposium on Computer, Consumer and Control, IS3C 2014 - Taichung, Taiwan, Province of China
Duration: 10 Jun 201412 Jun 2014

Publication series

NameProceedings - 2014 International Symposium on Computer, Consumer and Control, IS3C 2014

Conference

Conference2nd International Symposium on Computer, Consumer and Control, IS3C 2014
Country/TerritoryTaiwan, Province of China
CityTaichung
Period10/06/1412/06/14

Keywords

  • Electroencephalography
  • Eye State Identification
  • Incremental Attribute Learning
  • Neural Networks
  • Time Series Classification

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