Tetiana I. Aksenova

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Recently, the N-way partial least squares (NPLS) approach was reported as an effective tool for neuronal signal decoding and brain-computer interface (BCI) system calibration. This method simultaneously analyzes data in several domains. It combines the projection of a data tensor to a low dimensional space with linear regression. In this paper the(More)
In the article tensor-input/tensor-output blockwise Recursive N-way Partial Least Squares (RNPLS) regression is considered. It combines the multi-way tensors decomposition with a consecutive calculation scheme and allows blockwise treatment of tensor data arrays with huge dimensions, as well as the adaptive modeling of time-dependent processes with tensor(More)
The goal of the CLINATEC® Brain Computer Interface (BCI) Project is to improve tetraplegic subjects' quality of life by allowing them to interact with their environment through the control of effectors, such as an exoskeleton. The BCI platform is based on a wireless 64-channel ElectroCorticoGram (ECoG) recording implant WIMAGINE®, designed for long-term(More)
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(More)
BACKGROUND Brain Computer Interface (BCI) studies are performed in an increasing number of applications. Questions are raised about electrodes, data processing and effectors. Experiments are needed to solve these issues. OBJECTIVE To develop a simple BCI set-up to easier studies for improving the mathematical tools to process the ECoG to control an(More)
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