Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials
المؤلفون المشاركون
Zeng, Hong
Shen, Junjie
Zheng, Wenming
Liu, Jia
Song, Aiguo
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-19
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
The topdown determined visual object perception refers to the ability of a person to identify a prespecified visual target.
This paper studies the technical foundation for measuring the target-perceptual ability in a guided visual search task, using the EEG-based brain imaging technique.
Specifically, it focuses on the feature representation learning problem for single-trial classification of fixation-related potentials (FRPs).
The existing methods either capture only first-order statistics while ignoring second-order statistics in data, or directly extract second-order statistics with covariance matrices estimated with raw FRPs that suffer from low signal-to-noise ratio.
In this paper, we propose a new representation learning pipeline involving a low-level convolution subnetwork followed by a high-level Riemannian manifold subnetwork, with a novel midlevel pooling layer bridging them.
In this way, the discriminative power of the first-order features can be increased by the convolution subnetwork, while the second-order information in the convolutional features could further be deeply learned with the subsequent Riemannian subnetwork.
In particular, the temporal ordering of FRPs is well preserved for the components in our pipeline, which is considered to be a valuable source of discriminant information.
The experimental results show that proposed approach leads to improved classification performance and robustness to lack of data over the state-of-the-art ones, thus making it appealing for practical applications in measuring the target-perceptual ability of cognitively impaired patients with the FRP technique.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zeng, Hong& Shen, Junjie& Zheng, Wenming& Song, Aiguo& Liu, Jia. 2020. Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1186487
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zeng, Hong…[et al.]. Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials. Journal of Healthcare Engineering No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1186487
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zeng, Hong& Shen, Junjie& Zheng, Wenming& Song, Aiguo& Liu, Jia. Toward Measuring Target Perception: First-Order and Second-Order Deep Network Pipeline for Classification of Fixation-Related Potentials. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1186487
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186487
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