Optimized Complex Network Method (OCNM) for Improving Accuracy of Measuring Human Attention in Single-Electrode Neurofeedback System
المؤلفون المشاركون
Wu, Zheng-Ping
Zhang, Wei
Zhao, Jing
Chen, Chun
Ji, Peng
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-03-03
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129376
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر