Foundation Nanosciences

Grenoble, France

Foundation Nanosciences

Grenoble, France

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Eliseyev A.,Foundation Nanosciences | Eliseyev A.,CEA Grenoble | Benabid A.-L.,CEA Grenoble | Aksenova T.,Foundation Nanosciences | Aksenova T.,CEA Grenoble
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

In the present article a Recursive Multi-Way PLS algorithm for adaptive calibration of a BCI system is proposed. It combines the NPLS tensors decomposition with a scheme of recursive calculation. This Recursive algorithm allows treating data arrays of huge dimension. In addition, adaptive calibration provides a fast adjustment of the BCI system to mild changes of the signal. The proposed algorithm was validated on artificial and real data sets. In comparison to generic Multi-Way PLS, the recursive algorithm demonstrates good performance and robustness. © 2011 Springer-Verlag.


Eliseyev A.,Foundation Nanosciences | Eliseyev A.,CEA Grenoble | Moro C.,CEA Grenoble | Faber J.,Foundation Nanosciences | And 8 more authors.
Journal of Neural Engineering | Year: 2012

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 L1-Penalized NPLS is proposed for sparse BCI system calibration, allowing uniting the projection technique with an effective selection of subset of features. The L1-Penalized NPLS was applied for the binary self-paced BCI system calibration, providing selection of electrodes subset. Our BCI system is designed for animal research, in particular for research in non-human primates. © 2012 IOP Publishing Ltd.


Eliseyev A.,Foundation Nanosciences | Eliseyev A.,CEA Grenoble | Moro C.,CEA Grenoble | Costecalde T.,CEA Grenoble | And 7 more authors.
Journal of Neural Engineering | Year: 2011

In this paper a tensor-based approach is developed for calibration of binary self-paced brain-computer interface (BCI) systems. In order to form the feature tensor, electrocorticograms, recorded during behavioral experiments in freely moving animals (rats), were mapped to the spatial-temporal-frequency space using the continuous wavelet transformation. An N-way partial least squares (NPLS) method is applied for tensor factorization and the prediction of a movement intention depending on neuronal activity. To cope with the huge feature tensor dimension, an iterative NPLS (INPLS) algorithm is proposed. Computational experiments demonstrated the good accuracy and robustness of INPLS. The algorithm does not depend on any prior neurophysiological knowledge and allows fully automatic system calibration and extraction of the BCI-related features. Based on the analysis of time intervals preceding the BCI events, the calibration procedure constructs a predictive model of control. The BCI system was validated by experiments in freely moving animals under conditions close to those in a natural environment. © 2011 IOP Publishing Ltd.


PubMed | Foundation Nanosciences
Type: Journal Article | Journal: Journal of neural engineering | Year: 2012

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 L1-Penalized NPLS is proposed for sparse BCI system calibration, allowing uniting the projection technique with an effective selection of subset of features. The L1-Penalized NPLS was applied for the binary self-paced BCI system calibration, providing selection of electrodes subset. Our BCI system is designed for animal research, in particular for research in non-human primates.

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