Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces
المؤلفون المشاركون
Nambu, Isao
Wada, Yasuhiro
Carabez, Eduardo
Sugi, Miho
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2017، العدد 2017 (31 ديسمبر/كانون الأول 2017)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2017-11-07
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
From allowing basic communication to move through an environment, several attempts are being made in the field of brain-computer interfaces (BCI) to assist people that somehow find it difficult or impossible to perform certain activities.
Focusing on these people as potential users of BCI, we obtained electroencephalogram (EEG) readings from nine healthy subjects who were presented with auditory stimuli via earphones from six different virtual directions.
We presented the stimuli following the oddball paradigm to elicit P300 waves within the subject’s brain activity for later identification and classification using convolutional neural networks (CNN).
The CNN models are given a novel single trial three-dimensional (3D) representation of the EEG data as an input, maintaining temporal and spatial information as close to the experimental setup as possible, a relevant characteristic as eliciting P300 has been shown to cause stronger activity in certain brain regions.
Here, we present the results of CNN models using the proposed 3D input for three different stimuli presentation time intervals (500, 400, and 300 ms) and compare them to previous studies and other common classifiers.
Our results show >80% accuracy for all the CNN models using the proposed 3D input in single trial P300 classification.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Carabez, Eduardo& Sugi, Miho& Nambu, Isao& Wada, Yasuhiro. 2017. Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141144
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Carabez, Eduardo…[et al.]. Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1141144
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Carabez, Eduardo& Sugi, Miho& Nambu, Isao& Wada, Yasuhiro. Convolutional Neural Networks with 3D Input for P300 Identification in Auditory Brain-Computer Interfaces. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1141144
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1141144
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