Open AccessPenalized Multi-Way Partial Least Squares for Smooth Trajectory Decoding from Electrocorticographic (ECoG) Recording
Author(s) -
Andrey Eliseyev,
Tetiana Aksenova
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0154878
Subject(s) - smoothness , kalman filter , partial least squares regression , computer science , trajectory , algorithm , decoding methods , recursive least squares filter , least squares function approximation , polynomial , lasso (programming language) , artificial intelligence , mathematics , adaptive filter , machine learning , statistics , mathematical analysis , physics , astronomy , estimator , world wide web
In the current paper the decoding algorithms for motor-related BCI systems for continuous upper limb trajectory prediction are considered. Two methods for the smooth prediction, namely Sobolev and Polynomial Penalized Multi-Way Partial Least Squares (PLS) regressions, are proposed. The methods are compared to the Multi-Way Partial Least Squares and Kalman Filter approaches. The comparison demonstrated that the proposed methods combined the prediction accuracy of the algorithms of the PLS family and trajectory smoothness of the Kalman Filter. In addition, the prediction delay is significantly lower for the proposed algorithms than for the Kalman Filter approach. The proposed methods could be applied in a wide range of applications beyond neuroscience.