Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies
المؤلفون المشاركون
Vázquez, Roberto A.
Salazar-Varas, R.
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-20
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance.
Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification.
In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability.
Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected.
Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day.
The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications.
Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated.
This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks.
By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model.
Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification.
The increase rate is approximately of 17%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Salazar-Varas, R.& Vázquez, Roberto A.. 2019. Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129661
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Salazar-Varas, R.& Vázquez, Roberto A.. Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1129661
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Salazar-Varas, R.& Vázquez, Roberto A.. Facing High EEG Signals Variability during Classification Using Fractal Dimension and Different Cutoff Frequencies. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1129661
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1129661
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