L1-Penalized N-way PLS for subset of electrodes selection in BCI experiments

Eliseyev, Andrey; Moro, Cécile; Faber, Jean; Wyss, Alexander F.; Torres, Napoléon; Mestais, C.; Benabid, Alim Louis; Aksenova, Tetiana

doi:10.1088/1741-2560/9/4/045010

Cited by 33 publications

(32 citation statements)

References 48 publications

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“…To comprehensively assess efficiencies of the proposed methods, Table II investigates the computational time of the MCCA and the L1-MCCA for reference signal optimization, TABLE II COMPUTATIONAL TIME OF THE MCCA AND THE L1-MCCA FOR REFERENCE SIGNAL OPTIMIZATION, AND THAT OF THE CCA FOR SSVEP RECOGNITION, EVALUATED FROM THE LEAVE-ONE- Collaboratively multiway optimization has been suggested to be more promising than the one-way optimization for EEG data analysis [39]- [43] and also for electrocorticogram (ECoG) data analysis [44], [45]. Recently, regularized tensor discriminant analysis and penalized -way partial least squares have began to emerge and shown great potentials for the BCI application [46], [47]. In this study, the proposed L1-MCCA effectively combines both the multiway canonical correlation analysis and L1-regularization technique, which significantly improves the classification accuracy of SSVEP-based BCI.…”

Section: Discussionmentioning

confidence: 99%

L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

Zhang

Zhou

Jin

et al. 2013

IEEE Trans. Neural Syst. Rehabil. Eng.

238

151

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Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.

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Section: Discussionmentioning

confidence: 99%

L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

Zhang

Zhou

Jin

et al. 2013

IEEE Trans. Neural Syst. Rehabil. Eng.

238

151

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“…Moreover, the influence of each modality on the prediction model could be studied 33 . In addition, different modalities could be independently penalized to introduce special properties to the modeling tasks, e.g., a sparse solution in the spatial domain for selection of the most informative electrodes subset 34 . Finally, as demonstrated previously 35 , the tensor-based methods could improve the noise-robustness versus the vector-oriented approaches that is of great importance for BCI tasks.…”

Section: Introductionmentioning

confidence: 99%

Recursive Exponentially Weighted N-way Partial Least Squares Regression with Recursive-Validation of Hyper-Parameters in Brain-Computer Interface Applications

Eliseyev

Auboiroux

Costecalde

et al. 2017

Sci Rep

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A tensor-input/tensor-output Recursive Exponentially Weighted N-Way Partial Least Squares (REW-NPLS) regression algorithm is proposed for high dimension multi-way (tensor) data treatment and adaptive modeling of complex processes in real-time. The method unites fast and efficient calculation schemes of the Recursive Exponentially Weighted PLS with the robustness of tensor-based approaches. Moreover, contrary to other multi-way recursive algorithms, no loss of information occurs in the REW-NPLS. In addition, the Recursive-Validation method for recursive estimation of the hyper-parameters is proposed instead of conventional cross-validation procedure. The approach was then compared to state-of-the-art methods. The efficiency of the methods was tested in electrocorticography (ECoG) and magnetoencephalography (MEG) datasets. The algorithms are implemented in software suitable for real-time operation. Although the Brain-Computer Interface applications are used to demonstrate the methods, the proposed approaches could be efficiently used in a wide range of tasks beyond neuroscience uniting complex multi-modal data structures, adaptive modeling, and real-time computational requirements.

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“…In contrast, real wavelets return real time-frequency signals, which do not permit us to readily separate the amplitude and phase of the oscillating signals composing the analyzed signal (Torrence and Compo, 1998 ). The use of different wavelets has been investigated by, for example, Daubechies (Bhattacharyya et al, 2011 ; Bouton et al, 2016 ), Meyer (Eliseyev et al, 2012 ), Haar (Kousarrizi et al, 2009 ) or real (Chao et al, 2010 ; Bashashati et al, 2015 ), and complex (Lemm et al, 2004 ; Eliseyev and Aksenova, 2014 ) Morlet wavelets. More specifically, the relevance of different real and complex wavelets has been compared for kinematic decoding from ECoG signals in Eliseyev et al ( 2012 ).…”

Section: Feature Extractionmentioning

confidence: 99%

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Schaeffer

Aksenova

2018

Front. Neurosci.

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Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed.

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L1-Penalized N-way PLS for subset of electrodes selection in BCI experiments

Cited by 33 publications

References 48 publications

L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

Recursive Exponentially Weighted N-way Partial Least Squares Regression with Recursive-Validation of Hyper-Parameters in Brain-Computer Interface Applications

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

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