Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm

Bin Hu, Xiaowei Li, Shuting Sun, Martyn Ratcliffe

Research output: Contribution to journalArticlepeer-review

119 Citations (Scopus)

Abstract

The research detailed in this paper focuses on the processing of Electroencephalography (EEG) data to identify attention during the learning process. The identification of affect using our procedures is integrated into a simulated distance learning system that provides feedback to the user with respect to attention and concentration. The authors propose a classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm. To evaluate the CFS+KNN algorithm, it was test against CFS+C4.5 algorithm and other classification algorithms. The classification performance was measured 10 times with different 3-fold cross validation data. The data was derived from 10 subjects while they were attempting to learn material in a simulated distance learning environment. A self-Assessment model of self-report was used with a single valence to evaluate attention on 3 levels (high, neutral, low). It was found that CFS+KNN had a much better performance, giving the highest correct classification rate (CCR) of 80.84 ± 3.0 % for the valence dimension divided into three classes.

Original languageEnglish
Pages (from-to)38-45
Number of pages8
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes

Keywords

  • Attention recognition
  • CFS
  • EEG
  • KNN

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