title: Classification of multi-modal data in a self-paced binary BCI in freely moving animals.
abstract: The goal of the present article is to compare different classifiers using multi-modal data analysis in a binary self-paced BCI. Individual classifiers were applied to multi-modal neuronal data which was projected to a low dimensional space of latent variables using the Iterative N-way Partial Least Squares algorithm. To create a multi-way feature array, electrocorticograms (ECoG) recorded from animal brains were mapped to the spatial-temporal-frequency space using continuous wavelet transformation. To compare the classifiers BCI experiments were simulated. For this purpose we used 9 recordings from behavioral experiments previously recorded in rats free to move in a nature like environment.
authors.affiliation: CEA, LETI, CLINATEC, MINATEC Campus, 17 rue des Martyrs, 38054 Grenoble Cédex, France. andreyel@gmail.com
authors.forename: Andrey
authors.initials: A
authors.lastname: Eliseyev
authors.forename: Jean
authors.initials: J
authors.lastname: Faber
authors.forename: Tatiana
authors.initials: T
authors.lastname: Aksenova
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000473:pathology*; D004569:Electroencephalography / Q000379:methods*; D005189:False Positive Reactions; D016014:Linear Models; D008328:Man-Machine Systems; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology; D011237:Predictive Value of Tests; D051381:Rats; D012044:Regression Analysis; D015203:Reproducibility of Results; D014584:User-Computer Interface; D058067:Wavelet Analysis