Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation

Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation

The discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not...

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Main Authors: Qian Cai, Jianfeng Yan, Hongfang Han, Weiqiang Gong, Haixian Wang
Format: Article
Language:English
Published: Hindawi Limited 2021-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2021/6634672
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spelling doaj-f6f72ba3bb354536866b8fd8849748f92021-06-07T02:12:39ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732021-01-01202110.1155/2021/6634672Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor RepresentationQian Cai0Jianfeng Yan1Hongfang Han2Weiqiang Gong3Haixian Wang4School of Statistics and MathematicsKey Laboratory of Child Development and Learning Science of Ministry of EducationKey Laboratory of Child Development and Learning Science of Ministry of EducationNanjing Les Information System Technology Company Ltd.Key Laboratory of Child Development and Learning Science of Ministry of EducationThe discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not make full use of the information in frequency domain. The paper presents multilinear discriminative spatial patterns (MDSP) to derive multiple interrelated lower dimensional discriminative subspaces of low frequency movement-related cortical potential (MRCP). Experimental results on two finger movement tasks’ EEG datasets demonstrate the effectiveness of the proposed MDSP method.http://dx.doi.org/10.1155/2021/6634672
collection DOAJ
language English
format Article
sources DOAJ
author Qian Cai
Jianfeng Yan
Hongfang Han
Weiqiang Gong
Haixian Wang
spellingShingle Qian Cai
Jianfeng Yan
Hongfang Han
Weiqiang Gong
Haixian Wang
Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
Computational Intelligence and Neuroscience
author_facet Qian Cai
Jianfeng Yan
Hongfang Han
Weiqiang Gong
Haixian Wang
author_sort Qian Cai
title Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
title_short Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
title_full Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
title_fullStr Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
title_full_unstemmed Multilinear Discriminative Spatial Patterns for Movement-Related Cortical Potential Based on EEG Classification with Tensor Representation
title_sort multilinear discriminative spatial patterns for movement-related cortical potential based on eeg classification with tensor representation
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5273
publishDate 2021-01-01
description The discriminative spatial patterns (DSP) algorithm is a classical and effective feature extraction technique for decoding of voluntary finger premovements from electroencephalography (EEG). As a purely data-driven subspace learning algorithm, DSP essentially is a spatial-domain filter and does not make full use of the information in frequency domain. The paper presents multilinear discriminative spatial patterns (MDSP) to derive multiple interrelated lower dimensional discriminative subspaces of low frequency movement-related cortical potential (MRCP). Experimental results on two finger movement tasks’ EEG datasets demonstrate the effectiveness of the proposed MDSP method.
url http://dx.doi.org/10.1155/2021/6634672
work_keys_str_mv AT qiancai multilineardiscriminativespatialpatternsformovementrelatedcorticalpotentialbasedoneegclassificationwithtensorrepresentation
AT jianfengyan multilineardiscriminativespatialpatternsformovementrelatedcorticalpotentialbasedoneegclassificationwithtensorrepresentation
AT hongfanghan multilineardiscriminativespatialpatternsformovementrelatedcorticalpotentialbasedoneegclassificationwithtensorrepresentation
AT weiqianggong multilineardiscriminativespatialpatternsformovementrelatedcorticalpotentialbasedoneegclassificationwithtensorrepresentation
AT haixianwang multilineardiscriminativespatialpatternsformovementrelatedcorticalpotentialbasedoneegclassificationwithtensorrepresentation
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