Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
Joint Authors
Wu, Zheng-Ping
Zhang, Wei
Zhao, Jing
Chen, Chun
Ji, Peng
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-03-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
A neurofeedback system adjusting an individual’s attention is an effective treatment for attention-deficit/hyperactivity disorder (ADHD).
In current studies, an accurate measure of the level of human attention is one of the key issues that arouse much interest.
This paper proposes a novel optimized complex network method (OCNM) for measuring an individual’s attention level using single-electrode electroencephalography (EEG) signals.
A time-delay embedding algorithm was used to reconstruct EEG data epochs into nodes of the OCNM network.
Euclidean distances were calculated between each two nodes to decide edges of the network.
Three key parameters influencing OCNM, i.e., delaying time, embedding dimension, and connection threshold, were optimized for each individual.
The average degree and clustering coefficient of the constructed network were extracted as a feature vector and were classified into two patterns of concentration and relaxation using an LDA classifier.
In the offline experiments of six subjects, the classification performance was tested and compared with an attention meter method (AMM) and an α + β + δ + θ + R method.
The experimental results showed that the proposed OCNM achieved the highest accuracy rate (80.67% versus 70.58% and 68.88%).
This suggests that the proposed method can potentially be used for EEG-based neurofeedback systems with a single electrode.
American Psychological Association (APA)
Wu, Zheng-Ping& Zhang, Wei& Zhao, Jing& Chen, Chun& Ji, Peng. 2019. Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129376
Modern Language Association (MLA)
Wu, Zheng-Ping…[et al.]. Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1129376
American Medical Association (AMA)
Wu, Zheng-Ping& Zhang, Wei& Zhao, Jing& Chen, Chun& Ji, Peng. Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1129376
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129376