Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks
المؤلفون المشاركون
Xu, Guanghua
Chen, Longting
Zhang, Kai
Tian, Peiyuan
Han, ChengCheng
Zhang, Sicong
Duan, Nan
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-28
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
In the process of brain-computer interface (BCI), variations across sessions/subjects result in differences in the properties of potential of the brain.
This issue may lead to variations in feature distribution of electroencephalogram (EEG) across subjects, which greatly reduces the generalization ability of a classifier.
Although subject-dependent (SD) strategy provides a promising way to solve the problem of personalized classification, it cannot achieve expected performance due to the limitation of the amount of data especially for a deep neural network (DNN) classification model.
Herein, we propose an instance transfer subject-independent (ITSD) framework combined with a convolutional neural network (CNN) to improve the classification accuracy of the model during motor imagery (MI) task.
The proposed framework consists of the following steps.
Firstly, an instance transfer learning based on the perceptive Hash algorithm is proposed to measure similarity of spectrogram EEG signals between different subjects.
Then, we develop a CNN to decode these signals after instance transfer learning.
Next, the performance of classifications by different training strategies (subject-independent- (SI-) CNN, SD-CNN, and ITSD-CNN) are compared.
To verify the effectiveness of the algorithm, we evaluate it on the dataset of BCI competition IV-2b.
Experiments show that the instance transfer learning can achieve positive instance transfer using a CNN classification model.
Among the three different training strategies, the average classification accuracy of ITSD-CNN can achieve 94.7±2.6 and obtain obvious improvement compared with a contrast model p<0.01.
Compared with other methods proposed in previous research, the framework of ITSD-CNN outperforms the state-of-the-art classification methods with a mean kappa value of 0.664.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhang, Kai& Xu, Guanghua& Chen, Longting& Tian, Peiyuan& Han, ChengCheng& Zhang, Sicong…[et al.]. 2020. Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139345
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhang, Kai…[et al.]. Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1139345
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhang, Kai& Xu, Guanghua& Chen, Longting& Tian, Peiyuan& Han, ChengCheng& Zhang, Sicong…[et al.]. Instance Transfer Subject-Dependent Strategy for Motor Imagery Signal Classification Using Deep Convolutional Neural Networks. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1139345
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1139345
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