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Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System

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 optim...

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Detalles Bibliográficos
Autores principales: Wu, Zheng-Ping, Zhang, Wei, Zhao, Jing, Chen, Chun, Ji, Peng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421751/
https://www.ncbi.nlm.nih.gov/pubmed/30944553
http://dx.doi.org/10.1155/2019/2167871
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author Wu, Zheng-Ping
Zhang, Wei
Zhao, Jing
Chen, Chun
Ji, Peng
author_facet Wu, Zheng-Ping
Zhang, Wei
Zhao, Jing
Chen, Chun
Ji, Peng
author_sort Wu, Zheng-Ping
collection PubMed
description 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.
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spelling pubmed-64217512019-04-03 Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System Wu, Zheng-Ping Zhang, Wei Zhao, Jing Chen, Chun Ji, Peng Comput Intell Neurosci Research Article 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. Hindawi 2019-03-03 /pmc/articles/PMC6421751/ /pubmed/30944553 http://dx.doi.org/10.1155/2019/2167871 Text en Copyright © 2019 Zheng-Ping Wu et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
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
title Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
title_full Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
title_fullStr Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
title_full_unstemmed Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
title_short Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
title_sort optimized complex network method (ocnm) for improving accuracy of measuring human attention in single-electrode neurofeedback system
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6421751/
https://www.ncbi.nlm.nih.gov/pubmed/30944553
http://dx.doi.org/10.1155/2019/2167871
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