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Multi-channel fusion convolutional neural network to classify syntactic anomaly from language-related ERP components

Title
Multi-channel fusion convolutional neural network to classify syntactic anomaly from language-related ERP components
Authors
Khan A.Sung J.E.Kang J.-W.
Ewha Authors
성지은강제원
SCOPUS Author ID
성지은scopus; 강제원scopus
Issue Date
2019
Journal Title
Information Fusion
ISSN
1566-2535JCR Link
Citation
Information Fusion vol. 52, pp. 53 - 61
Keywords
Convolutional neural networkEvent-related potential signalsLinguistic feature fusionNeurological signal processingSentence classification
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS scopus
Document Type
Article
Abstract
Event-Related Potential (ERP) analyses have revealed several language-related components in sentence processing literature. More recently, researchers attempted to apply machine-learning techniques to classify the language-structure dependent ERP signals in a more reliable and efficient way. The purpose of the current paper is to propose a classification technique based on data-driven approach to detect syntactic anomaly from language-related ERP components. We specifically examined whether sentences with syntactic violations elicited differential patterns of ERP signals and the abnormal patterns can be reliably classified by machine-learning techniques. The specific aim of the study is to develop a multi-channel fusion convolutional neural network (MCF-CNN) including two branches of CNNs and a trunk merged by an intermediate fusion layer to obtain trained linguistic features from the raw data and perform the classification. We extracted different linguistic ERP components from syntactic violations and put them in the fusion. As a next procedure we combined the features in the fusion layer of the proposed neural network architecture. Experimental results demonstrate that the proposed method provides more than 92% classification accuracy. © 2018 Elsevier B.V.
DOI
10.1016/j.inffus.2018.10.008
Appears in Collections:
사범대학 > 언어병리학과 > Journal papers
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