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

Qian Cai, Jianfeng Yan, Hongfang Han, Weiqiang Gong, Haixian Wang

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Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Qian CaiSchool of Statistics and Mathematics, Nanjing Audit University, Nanjing 211815, Jiangsu, China.

Jianfeng YanKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.ORCID https://orcid.org/0000-0002-2507-6357

Hongfang HanKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.

Weiqiang GongNanjing Les Information System Technology Company Ltd., Nanjing 210007, Jiangsu, China.

Haixian WangKey Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, China.ORCID https://orcid.org/0000-0001-8220-9737

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Brain-Computer InterfacesElectroencephalographyAlgorithmsFingersMovement

Identifiers

PMID34135952

DOI10.1155/2021/6634672

PMCPMC8175166

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.