research-article

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

Authors:

Martyn RatcliffeAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 15, Issue 1

Pages 38 - 45

https://doi.org/10.1109/TCBB.2016.2616395

Published: 01 January 2018 Publication History

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 $80.84 \pm 3.0$% for the valence dimension divided into three classes.

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Cited By

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Li JZhao R(2022)Integrated Classification Algorithm for Unbalanced Data Streams Based on Joint Nonnegative Matrix FactorizationWireless Communications & Mobile Computing10.1155/2022/56599792022Online publication date: 1-Jan-2022
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Index Terms

Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning algorithms
      1. Feature selection

Index terms have been assigned to the content through auto-classification.

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cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 15, Issue 1

January 2018

352 pages

ISSN:1545-5963

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Copyright © 2018.

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IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 January 2018

Published in TCBB Volume 15, Issue 1

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https://dl.acm.org/doi/10.1155/2022/6000989
Fu JZhou XMei G(2022)Internet Digital Economy Development Forecast Based on Artificial Intelligence and SVM-KNN Network DetectionComputational Intelligence and Neuroscience10.1155/2022/57926942022Online publication date: 1-Jan-2022
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Liu XArdakani S(2022)A machine learning enabled affective E-learning system modelEducation and Information Technologies10.1007/s10639-022-11010-x27:7(9913-9934)Online publication date: 1-Aug-2022
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Chen HSun SLi JYu RLi NLi XHu B(2021)Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion RecognitionIEEE Transactions on Affective Computing10.1109/TAFFC.2021.313785714:3(2077-2088)Online publication date: 23-Dec-2021
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