Tetiana Aksenova

Tetiana Aksenova
Atomic Energy and Alternative Energies Commission | CEA · Clinatec

PhD, HDR

About

101
Publications
12,925
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706
Citations
Additional affiliations
November 2008 - present
Cea Leti
Position
  • --
January 2002 - October 2007
INSERM U318
Position
  • INSERM
January 2002 - December 2007

Publications

Publications (101)
Preprint
In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to big datasets. This may influence the BCI system performance as machine learning methods depend strongly on the training dataset size. Important questions arise: taking into account neuronal signal characteristics (e.g., non-stationari...
Article
Objective: The article aims at addressing 2 challenges to step motor BCI out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach: Closed-loop adaptive/increm...
Article
Full-text available
The Eighth International Brain-Computer Interface (BCI) Meeting was held June 7-9, 2021 in a virtual format. The conference continued the BCI Meeting series' interactive nature with 21 workshops covering the breadth of topics in BCI (also called brain-machine interface) research. Some workshops provided detailed examinations of methods, hardware, o...
Preprint
Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications. A key one persists in the high performance control of diverse effectors for complex tasks, using chronic and safe recorders. This control must be robust over time and of high decoding performance without continuous recalibra...
Preprint
Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs emerge as a good compromise betw...
Article
Aims: The long-term clinical validation of ABCDEFGH, a wireless 64-channel epidural ElectroCorticoGram (ECoG) recorder was investigated. The ABCDEFGH device was implanted in two quadriplegic patients within the context of a Brain-Computer Interface (BCI) protocol. This study focused on the ECoG signal quality and stability in two patients bilatera...
Preprint
Objective. Brain-computer interfaces (BCIs) create a new communication pathway between the brain and an effector without neuromuscular activation. BCI experiments highlighted high intra and inter-subjects variability in the BCI decoders. Although BCI model is generally relying on neurological markers generalizable on the majority of subjects, it re...
Conference Paper
Quantifying and evaluating properly the performances is a critical issue in BCI experiments. The choice of the most adapted metrics can be difficult because they are specific to the experimental paradigm, task control, and data. In the current study evaluation criteria for closed-loop adaptive dynamic and hierarchical discrete-continuous brain-comp...
Article
Full-text available
Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscil...
Article
Background Approximately 20% of traumatic cervical spinal cord injuries result in tetraplegia. Neuroprosthetics are being developed to manage this condition and thus improve the lives of patients. We aimed to test the feasibility of a semi-invasive technique that uses brain signals to drive an exoskeleton. Methods We recruited two participants at C...
Article
Full-text available
This article deals with the long-term preclinical validation of WIMAGINE® (Wireless Implantable Multi-channel Acquisition system for Generic Interface with Neurons), a 64-channel wireless implantable recorder that measures the electrical activity at the cortical surface (electrocorticography, ECoG). The WIMAGINE® implant was designed for chronic wi...
Article
Full-text available
Brain-computer interface (BCI) systems may require the user to perform a set of mental tasks, such as imagining different types of motion. The performance demonstrated on these tasks varies with time and between users. This study presents a new method for the automatically adaptive, user-specific generation of a sequence of tasks to increase the ef...
Article
Full-text available
Among biomedical signals, repetitive or quasi-periodic signals are particularly widespread. While the periodic component is still presented these signals are characterized by period variations (fundamental frequency, amplitude, etc.). The lack of synchronization or phase shifts results in variations in similar segments’ durations, nominally identic...
Article
Full-text available
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....
Article
Full-text available
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...
Article
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 a...
Article
Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeleton...
Conference Paper
Full-text available
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 wi...
Conference Paper
Brain-Computer Interfaces (BCIs) are systems which convert brain neural activity into commands for external devices. BCI users generally alternate between No Control (NC) and Intentional Control (IC) periods. Numerous motor-related BCI decoders focus on the prediction of continuously-valued limb trajectories from neural signals. Although NC/IC disc...
Article
Full-text available
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...
Article
Full-text available
Long-term BCI based on chronic electrocorticogram (ECoG) is one of the challenges of BCI project at CLINATEC ® /LETI/CEA. Brain signal decoding algorithms are crucial to convert the preserved tetraplegia patient's brain activity into motor command for an exoskeleton. Robust model identification methods have been designed in CLINATEC ® , and have al...
Conference Paper
Full-text available
Our team has developed a fully implantable device called WIMAGINE®i to record ElectroCorticoGrams (ECoG) as a part of a Brain Computer Interface (BCI) platform to be used by quadriplegic subjectsii to control effectors with a large number of degrees of freedom, such as a 4-limb exoskeleton. Innovative ECoG signal decoding algorithms will allow self...
Article
Full-text available
The goal of CLINATEC® Brain Computer Interface Project is to improve tetraplegic subjects’ quality of life by allowing them to interact with their environment through the control of effectors with multiple degrees of freedom after training. Thanks to a long-term wireless 64-channel ECoG recording implant WIMAGINE® (Wireless Implantable Multi-channe...
Article
Full-text available
Objective: The key criterion for reliability of brain-computer interface (BCI) devices is their stability and robustness in natural environments in the presence of spurious signals and artifacts. Approach: To improve stability and robustness, a generalized additive model (GAM) is proposed for BCI decoder identification. Together with partial lea...
Conference Paper
Full-text available
The multi-way decoding algorithms are adapted to incomplete wirelessly transmitted data and integrated to CLINATEC® BCI platform. The platform includes wireless 64-channels ElectroCorticoGram (ECoG) recording implant WIMAGINE® and BCI software environment associated to a 4-limbs exoskeleton EMY.
Conference Paper
Full-text available
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...
Patent
The present invention relates to a method for filtering the signal of neuronal activity during a high frequency deep brain stimulation (DBS) to remove the stimulus artefact in the observed signal, comprising the step of approximating the observed signal trajectories in phase space the observed signal being considered as a sum of the stimulation art...
Article
Full-text available
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...
Article
Full-text available
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...
Article
In the present article a Recursive Multi-Way Partial Least Square Regression (RNPLS) algorithm for recursive tensor factorization and multi-linear regression is considered and tested in the model experiments. The method combines the Multi-Way Partial Least Square (NPLS) tensors decomposition with a scheme of recursive calculation. This recursive al...
Article
Full-text available
Unlabelled: Brain-computer interfaces (BCIs) include stimulators, infusion devices, and neuroprostheses. They all belong to functional neurosurgery. Deep brain stimulators (DBS) are widely used for therapy and are in need of innovative evolutions. Robotized exoskeletons require BCIs able to drive up to 26 degrees of freedom (DoF). We report the na...
Conference Paper
Full-text available
Recently N-way Partial Least Squares (NPLS) were reported as an effective tool for neuronal signal decoding and BCI system calibration. This method simultaneously analyses data in several domains. It is based on the projection of a data tensor to a low dimensional space using all variables to create a final model. In the present paper the L1-Penali...
Article
Full-text available
The goal of the present article is to compare different classifiers using multi-modal data analysis in a binary self-paced BCI. Individual classifiers were applied to multi-modal neuronal data which was projected to a low dimensional space of latent variables using the Iterative N-way Partial Least Squares algorithm. To create a multi-way feature a...
Article
Full-text available
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 transformat...
Conference Paper
Full-text available
The robust regression analysis works on data affected by deviations from a general assumption of normality. Currently the field of robust linear regression analysis is well developed and there are number of stable and verified by time methods. In contrast the robust structural modeling and high-order model parameter estimation are still under activ...
Article
Brain Computer Interface (BCI) aims to provide a way for effectors control based on measurements of brain electrical activity. Majority of current BCI systems is driven by the external cues to distinguish informative data periods from brain’s general functioning (cue-paced BCI). This restriction seems to be extremely burdensome in the real-life app...
Article
Full-text available
The robust regression analysis works on data affected by deviations from a general assumption of normality. There are number of stable and robust methods in the field of linear regression analysis. In contrast the robust structural modeling is still under active development. This paper describes a novel algorithm designed to solve a task of optimal...
Article
Full-text available
La réalisation d'une interface cerveau machine EEG nécessite généralement l'utilisation d'un grand nombre d'électrodes, causant la gêne de l'utilisateur et augmentant considérablement le coût calculatoire des traitements. Cependant, un choix judicieux de l'emplacement des ces électrodes peut permettre une réduction importante de leur nombre sans pe...
Article
Full-text available
This letter is devoted to the suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An approach based on nonlinear adaptive model with self-oscillations is proposed. We developed an algorithm of adaptive filtering based on this approach. The proposed algorithm was tested using recordings collected...
Article
Full-text available
Common spatial pattern (CSP) is becoming a standard way to combine linearly multi-channel EEG data in order to increase discrimination between two motor imagery tasks. We demonstrate in this article that the use of robust estimates allow improving the quality of CSP decomposition and CSP-based BCI. Furthermore, an evolutionary algorithm (EA)-type f...
Conference Paper
Full-text available
The present paper is devoted to the suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An algorithm of adaptive filtering based on the signals synchronization in phase space is presented. The algorithm was implemented and tested using synthetic data and recordings collected from patients during...
Article
The polynomial iteration algorithm to realize the robust parametric and structural estimation within the frame of GMDH technique is presented. The two-level neural network structure with the controlled model complexity provides the computational stability of GMDH-PNN algorithm. The computational experiment demonstrating the parametric and structura...
Article
Full-text available
The study of EEG recordings during the interval prior to an epileptic seizure onset--the preictal period--is likely to detect changes in the ongoing brain activity consistent with seizure anticipation. A novel index of spectral instability (ISpI) based on multiple abrupt changes of EEG spectral features is presented here. Based on the analysis of c...
Article
Full-text available
The present paper is devoted to suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An approach based on a nonlinear adaptive model with self‐oscillations is proposed.
Conference Paper
Full-text available
Fast and reliable unsupervised spike sorting is necessary for electrophysiological applications that require critical time operations (e.g., recordings during human neurosurgery) or management of large amount of data (e.g., recordings from large microelectrode arrays in behaving animals). We present an algorithm that can recognize the waveform of n...
Conference Paper
Full-text available
Multisite electrophysiological recordings have become a standard tool for exploring brain functions. These techniques point out the necessity of fast and reliable unsupervised spike sorting. We present an algorithm that performs on-line real-time spike sorting for data streaming from a data acquisition board or in off-line mode from a WAV formatted...
Article
Full-text available
The present study demonstrates the application of the Unsupervised Spike Sorting algorithm (USS) to separation of multi-unit recordings and investigation of neuronal activity patterns in the subthalamic nucleus (STN). This nucleus is the main target for deep brain stimulation (DBS) in Parkinsonian patients. The USS comprises a fast unsupervised lea...
Conference Paper
Full-text available
The paper presents a new version of a GMDH type algorithm able to perform an automatic model structure synthesis, robust model parameter estimation and model validation in presence of outliers. This algorithm allows controlling the complexity – number and maximal power of terms – in the models and provides stable results and computational efficienc...
Article
Full-text available
Purpose: An approach to the problem of seizure prediction aimed to provide a computationally effective method for real-time application with standard scalp EEG recordings is presented. Methods: One control record performed during steady interictal interval and 10 preseizure records lasting 15-20 minutes with standard scalp EEG 32 channels from 3 s...
Article
A description of computer implementation of an algorithm for classification of neuron impulses (ACNI) using nonlinear dynamic equations is given. ACNI includes an automated learning procedure and allows a real-time classification of impulses of several neurons, recorded by a single microelectrode. Results of algorithm testing on a simulated signal...
Article
The article presents a computer realization of an algorithm for neural spike sorting. The algorithm is based on use of nonlinear dynamic equations. The developed soft-ware includes an automatic learning procedure and allows real-time spikes sorting on a multiunit single electrode record. The algorithm was tested on simulated signals with different...
Conference Paper
Full-text available
The present study is devoted to the problem of automatic sorting of extracellularly recorded action potentials of neurons. The classification of spike waveform is considered as a pattern recognition problem of segments of signal that corresponds to the appearance of spikes. Nonlinear oscillating model with perturbation is used to describe the wavef...
Article
The present study introduces the method for solving the problem on early prediction of epilepsy seizure onset based on analysis of multi-channel electroencephalogram (EEG). This problem is considered as the problem of on-line detection of multiple abrupt changes in spectral characteristics of the process under consideration. With EEG characteristic...
Article
This article presents the Robust Polynomial Neural Networks, a self-organizing multilayered iterative GMDH-type algorithm that provides robust linear and nonlinear polynomial regression models. The accuracy of the algorithm is compared to traditional GMDH and the multiple linear regression analysis using artificial and real data sets in quantitativ...