SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features
المؤلفون المشاركون
Trigui, Omar
Daoud, Sawsan
Ghorbel, Mohamed
Mhiri, Chokri
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-08
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Brain-computer interface (BCI) systems translate the human neurophysiological activities into commands through EEG analysis.
Improving the BCI performances leads to faster and easier use and less fatigue.
In this study, we proposed a new prepossessing approach to increase the robustness of a steady-state visual evoked potential (SSVEP) based BCI.
Inspiring from the known properties of the SSVEP frequency components, the goal was to enhance the signal quality by making it more convenient to be interpreted by the decision-making step.
We first investigated the potential to detect the deteriorating periods based on the physiological properties of the SSVEP.
The proposed system localizes the intervals which can obscure the SSVEP frequencies by a new algorithm founded on the processing and the analysis of the instantaneous phase.
The piecewise linear regression allows a sampler comprehension of the phase signal.
Then, these intervals are filtered by the moving average filter to enhance the SSVEP quality.
Finally, the decision making is made by the canonical correlation analysis (CCA) algorithm.
The results of experiments, using real EEG signals from five subjects, show that the proposed approach significantly increases the performances in terms of accuracy and information transfer rate by about 7.3% and 3.85 bits/min, respectively, in case of 2 s segment length.
On the other hand, the spatial filtering methods of the literature weaken the system performances.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Trigui, Omar& Daoud, Sawsan& Ghorbel, Mohamed& Mhiri, Chokri. 2020. SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138809
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Trigui, Omar…[et al.]. SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1138809
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Trigui, Omar& Daoud, Sawsan& Ghorbel, Mohamed& Mhiri, Chokri. SSVEP Enhancement Using Moving Average Filter Controlled by Phase Features. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1138809
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138809
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