Vous êtes ici : Accueil > Kernel-based NPLS for continuous trajectory decoding from ECoG data for BCI applications

Publications

Kernel-based NPLS for continuous trajectory decoding from ECoG data for BCI applications

Publié le 29 mars 2018
Kernel-based NPLS for continuous trajectory decoding from ECoG data for BCI applications
Auteurs
Engel S., Aksenova T., Eliseyev A.
Year2017-0075
Source-TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Affiliations
Université Grenoble Alpes, Grenoble, France, CEA, LETI, CLINATEC, MINATEC Campus, Grenoble, France
Abstract
In this paper, nonlinearity is introduced to linear neural activity decoders to improve continuous hand trajectory prediction for Brain-Computer Interface systems. For decoding the high-dimensional data-tensor, a kernel regression was coupled with multilinear PLS (NPLS). Two ways to introduce nonlinearity were studied: a generalized linear model with kernel link function and kernel regression in the NPLS latent variables space (inside or outside the NPLS iterations). The efficiency of these approaches was tested on the publically available database of the simultaneous recordings of three-dimensional hand trajectories and epidural electrocorticogram (ECoG) signals of a Japanese macaque. Compared to linear methods, nonlinearity did not significantly improve the prediction accuracy but did significantly improve the smoothness of the prediction. © Springer International Publishing AG 2017.
Author-Keywords
 
Index-Keywords
Brain, Clustering algorithms, Decoding, Electrophysiology, Equivalence classes, Forecasting, Interfaces (computer), Neurons, Trajectories, Electrocorticogram (ECoG), Generalized linear model, High dimensional data, Kernel regression, Latent variable, Prediction accuracy, Simultaneous recording, Trajectory prediction, Brain computer interface
ISSN3029743
Lien vers articleLink

Retour à la liste