GAYRAUD , Nathalie

Non-invasive Brain Computer Interfaces (BCIs) allow a user to control a machine using only their brain activity. The BCI system acquires electroencephalographic (EEG) signals, characterized by a low signal-to-noise ratio and an important variability both across sessions and across users. Typically, the BCI system is calibrated before each use,...

This work provides a theoretical analysis framework for features that belong to the high dimensional Riemannian manifold of symmetric positive definite matrices. In non-invasive EEG-based Brain Computer Interfaces, such as the P300 speller, these are sample covariance matrices of the epoched EEG signal that are classified into two classes. An...

A code-modulated Visual Evoked Potential Brain Computer Interface (c-VEP BCI) allows for spelling from a virtual keyboard of flashing characters. All characters flash simultaneously, and each character flashes according to a predefined pseudo-random binary sequence, circular-shifted by a different time lag. For a given character, the...

Brain Computer Interfaces (BCIs) based on visual evoked potentials (VEP) allow for spelling from a keyboard of flashing characters. Among VEP BCIs, code-modulated visual evoked potentials (c-VEPs) are designed for high-speed communication . In c-VEPs, all characters flash simultaneously. In particular, each character flashes according to a...

Matching parcels across different subjects is an open problem in neuroscience. Even when produced by the same technique, parcellations tend to differ in the number, shape, and spatial localization of parcels across subject. To the best of our knowledge, no technique has been able to tackle this problem. In this work, we propose and compare four...

Introduction: Motor imagery (MI) based BCI systems record and analyze the brain activity to determine users' intentions while imagining moving some parts of their body. In order to build systems that are able to detect several commands, multiclass schemes need to be applied. Hierarchical methods allow solving multiclass problems by using a tree...

Hundreds of millions of general anesthesia are performed each year on patients all overthe world. Among these patients, 0.1–0.2% are victims of Accidental Awareness duringGeneral Anesthesia (AAGA), i.e., an unexpected awakening during a surgical procedureunder general anesthesia. Although anesthesiologists try to closely monitor patientsusing...

Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a...

Accidental Awareness during General Anesthesia (AAGA) occurs in 1-2% of high-risk practice patients and is responsible for severe psychological trauma, termed post-traumatic stress disorder (PTSD). Currently, monitoring techniques have limited accuracy in predicting or detecting AAGA. Since the first reflex of a patient experiencing AAGA is to...

Predicting a subject's ability to use the interface with good accuracy is one of the major issues in the motor Brain-Computer interface (BCI) domain. A few recent studies show that subjective questionnaires could be used to predict the performance of motor imagery (MI) based BCI. Indeed, the Kinesthetic and Visual Imagery Questionnaire (KVIQ),...

Matching structural parcels across different subjects is an open problem in neuroscience. Even when produced by the same technique , parcellations tend to differ in the number, shape, and spatial lo-calization of parcels across subjects. In this work, we propose a parcel matching method based on Optimal Transport. We test its performance by...