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Journal of Medical Signals and Sensors، جلد ۱۵، شماره ۳، صفحات ۱۰-۴۱۰۳
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چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Graphs Constructed from Instantaneous Amplitude and Phase of Electroencephalogram Successfully Differentiate Motor Imagery Tasks
چکیده انگلیسی مقاله Abstract Background:  Accurate classification of electroencephalogram (EEG) signals is challenging given the nonlinear and nonstationary nature of the data as well as subject-dependent variations. Graph signal processing (GSP) has shown promising results in the analysis of brain imaging data. Methods:  In this article, a GSP-based approach is presented that exploits instantaneous amplitude and phase coupling between EEG time series to decode motor imagery (MI) tasks. A graph spectral representation of the Hilbert-transformed EEG signals is obtained, in which simultaneous diagonalization of covariance matrices provides the basis of a subspace that differentiates two classes of right hand and right foot MI tasks. To determine the most discriminative subspace, an exploratory analysis was conducted in the spectral domain of the graphs by ranking the graph frequency components using a feature selection method. The selected features are fed into a binary support vector machine that predicts the label of the test trials. Results:  The performance of the proposed approach was evaluated on brain–computer interface competition III (IVa) dataset. Conclusions:  Experimental results reflect that brain functional connectivity graphs derived using the instantaneous amplitude and phase of the EEG signals show comparable performance with the best results reported on these data in the literature, indicating the efficiency of the proposed method compared to the state-of-the-art methods.
کلیدواژه‌های انگلیسی مقاله Electroencephalogram,graph signal processing,Hilbert transform,instantaneous amplitude and phase,motor imagery decoding
نویسندگان مقاله | Maliheh Miri
Department of Electrical Engineering, Yazd University, Yazd, Iran


| Vahid Abootalebi
Department of Electrical Engineering, Yazd University, Yazd, Iran


| Hamid Saeedi-Sourck
2.Neuro-X Institute, EPFL, Geneva, Switzerland 3.Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland


| Dimitri Van De Ville
Department of Biomedical Engineering, Lund University, Lund, Sweden


| Hamid Behjat


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/744
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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