Dimensionality reduction based on distance preservation to local mean for symmetric positive definite matrices and its application in brain-computer interfaces.
J Neural Eng
; 14(3): 036019, 2017 06.
Article
en En
| MEDLINE
| ID: mdl-28220764
OBJECTIVE: In this paper, we propose a nonlinear dimensionality reduction algorithm for the manifold of symmetric positive definite (SPD) matrices that considers the geometry of SPD matrices and provides a low-dimensional representation of the manifold with high class discrimination in a supervised or unsupervised manner. APPROACH: The proposed algorithm tries to preserve the local structure of the data by preserving distances to local means (DPLM) and also provides an implicit projection matrix. DPLM is linear in terms of the number of training samples. MAIN RESULTS: We performed several experiments on the multi-class dataset IIa from BCI competition IV and two other datasets from BCI competition III including datasets IIIa and IVa. The results show that our approach as dimensionality reduction technique-leads to superior results in comparison with other competitors in the related literature because of its robustness against outliers and the way it preserves the local geometry of the data. SIGNIFICANCE: The experiments confirm that the combination of DPLM with filter geodesic minimum distance to mean as the classifier leads to superior performance compared with the state of the art on brain-computer interface competition IV dataset IIa. Also the statistical analysis shows that our dimensionality reduction method performs significantly better than its competitors.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
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Reconocimiento de Normas Patrones Automatizadas
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Modelos Estadísticos
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Potenciales Evocados Motores
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Electroencefalografía
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Interfaces Cerebro-Computador
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Corteza Motora
Tipo de estudio:
Diagnostic_studies
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Evaluation_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Neural Eng
Asunto de la revista:
NEUROLOGIA
Año:
2017
Tipo del documento:
Article
Pais de publicación:
Reino Unido