Local discriminative spatial patterns for movement-related potentials-based EEG classification
被引:12
作者:
Wang, Haixian
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机构:
Southeast Univ, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R ChinaSoutheast Univ, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R China
Wang, Haixian
[1
]
Xu, Jiang
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机构:
Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R ChinaSoutheast Univ, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R China
Xu, Jiang
[2
]
机构:
[1] Southeast Univ, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R China
[2] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China
A novel discriminant method, termed local discriminative spatial patterns (LDSP), is proposed for movement-related potentials (MRPs)-based single-trial electroencephalogram (EEG) classification. Different from conventional discriminative spatial patterns (DSP), LDSP explicitly considers local structure of EEG trials in the construction of scatter matrices in the Fisher-like criterion. The underlying manifold structure of two-dimensional spatio-temporal EEG signals contains more discriminative information. LDSP is an extension to DSP in the sense that DSP can be formulated as a special case of LDSP. By constructing an adjacency matrix, LDSP is calculated as a generalized eigenvalue problem, and so is computationally straightforward. Experiments on MRPs-based single-trial EEG classification show the effectiveness of the proposed LDSP method. (C) 2010 Elsevier Ltd. All rights reserved.