Classification of covariance matrices using a Riemannian-based kernel for BCI applications

Neurocomputing

Volumn 112, Issue , 2013, Pages 172-178

  Barachant, Alexandre ^a,b   Bonnet, Stéphane ^a   Congedo, Marco ^b   Jutten, Christian ^b  

^a CEA LETI   (France)

^b UNIV GRENOBLE ALPES   (France)

Author keywords

Brain computer interfaces; Covariance matrix; Kernel; Riemannian geometry; Support vector machine

Indexed keywords

BRAIN-COMPUTER INTERFACE APPLICATIONS; COVARIANCE MATRICES; KERNEL; RIEMANNIAN GEOMETRY; SPATIAL COVARIANCE MATRIX; SPATIAL FILTERINGS; STATE OF THE ART; SYMMETRIC POSITIVE DEFINITE MATRICES;

COVARIANCE MATRIX; GEOMETRY; INTERFACES (COMPUTER); SUPPORT VECTOR MACHINES;

BRAIN COMPUTER INTERFACE;

ANALYSIS OF COVARIANCE; ARTICLE; BRAIN COMPUTER INTERFACE; CLASSIFICATION ALGORITHM; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; COST BENEFIT ANALYSIS; INTERMETHOD COMPARISON; KERNEL METHOD; MATHEMATICAL MODEL; MEASUREMENT ACCURACY; MENTAL TASK; PRIORITY JOURNAL; REIMANNIAN GEOMETRY; SIGNAL PROCESSING; SPATIAL ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 84877605991     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.12.039     Document Type: Article

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