Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI
المؤلفون المشاركون
Huang, Zhihua
Li, Minghong
Ma, Yuanye
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-02
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller.
Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet.
In this space, Fisher Criterion is used to measure the difference between target and nontarget ones.
Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors.
To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out.
The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters.
Our method achieved higher accuracies of classification and detection.
The ITRs also reflected the superiority of our method.
The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers.
The average feedback time for one round of EEG epochs was 1.57 ms.
The proposed method can improve the performance of P300 Speller BCI.
Its parallel computing scheme is able to support fast feedback required by online experiments.
The number of repeated stimuli can be significantly reduced by our method.
The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Huang, Zhihua& Li, Minghong& Ma, Yuanye. 2018. Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1131977
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Huang, Zhihua…[et al.]. Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1131977
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Huang, Zhihua& Li, Minghong& Ma, Yuanye. Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1131977
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131977
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