Perceptual anticipation is known to have an impact on the reaction time of decisions. While anticipatory brain activity patterns have been identified in human EEG, in particular in the form of Contingent-Negative Variations (CNV), the single-trial neural signature of anticipation remains unexplored. Similarly, past studies have underlined an effect of pre-stimulus alpha-band activity on reaction times. Still, it remains unknown whether this activity is stimulus-specific or rather acts as a general indicator of readiness. This study aimed to decipher whether human participants expected a visual or an auditory stimulus at the single-trial level in both cued and uncued trials. We show that the CNV entails information about the expected upcoming stimulus, and the information content can be extracted at the single-trial level. Behavioral analyses additionally indicate the correct classification of uncued trials.Competing Interest StatementThe authors have declared no competing interest.
Holographic EEG: multi-view deep learning for BCI
Igor Carrara, Bruno Aristimunha, Sylvain Chevallier, and 3 more authors
In CORTICO, May 2023
Published: Journée CORTICO 2023 - COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur
Dimensionality Reduction and Frequency Bin Optimization To Improve a Riemannian-based Classification Pipeline
Rune Frateur, Sylvain Chevallier, Florian Yger, and 1 more author
In CORTICO, May 2023
Published: CORTICO 2023 - Journées COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur
Abstract The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism, straightforward interpretation, and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations to capture inter-trial dependency and dynamics at the single-trial level. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the fitting accuracy of our model is comparable to the accuracy of the DDM, with the non-linear model performing better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Our model paves the way toward more accurately analyzing single-trial dynamics for perceptual decisions and accounts for pre- and post-stimulus influences.
User-specific frequency band and time segment selection with high class distinctiveness for Riemannian BCIs
This short technical report describes the approach submitted to the Clinical BCI Challenge-WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies on Riemannian Geometry, with a twist. Instead of using the classical covariance matrices, we also rely on measures of functional connectivity. Our approach ranked 1st on the task 1 of the competition.
2020
SMR/Theta Neurofeedback Training Improves Cognitive Performance and EEG Activity in Elderly With Mild Cognitive Impairment: A Pilot Study
Fabienne Marlats, Guillaume Bao, Sylvain Chevallier, and 6 more authors
Randomized Controlled Study Evaluating Efficiency of Low Intensity Transcranial Direct Current Stimulation (tDCS) for Dyspnea Relief in Mechanically Ventilated COVID-19 Patients in ICU: The tDCS-DYSP-COVID Protocol
Les progrès en analyse des signaux cérébraux pour un décodage en temps réel ont permis des avancées importantes pour les interfaces cérébrales. Ces interfaces cérébrales ou brain-computer interfaces (BCI) permettent de contrôler ou d’échanger des commandes avec un système en nécessitant pas ou peu de capacités physiques. Elles offrent une solution adaptée pour les personnes en situation de handicap. Dans leur forme actuelle, elles requièrent toutefois une bonne capacité de concentration pour fonctionner correctement. Ces BCI ont connu un rapide essor grâce aux méthodes d’apprentissage statistique, mais elles font face à deux verrous scientifiques. Le premier concerne la puissance du signal, qui est très faible, et les bruits environnants qui contaminent les enregistrements de signaux cérébraux, avec des niveaux bien supérieurs à celui du signal d’intérêt. Le second point concerne les variabilités individuelles qui peuvent réduire les algorithmes à des prédictions au niveau de la chance pour 10 à 20}% des sujets. Les travaux présentés dans ce manuscrit abordent les aspects tant expérimentaux que théoriques pour aborder ces deux problèmes. Les approches en traitement du signal utilisées pour travailler sur les signaux cérébraux peuvent dans certains cas s’appliquer sur d’autres types séries temporelles, par exemple pour des applications industrielles sur la détection d’anomalie. Toutes ces approches sont développées avec des outils libres pour une science ouverte et diffusable, en apportant quand c’est possible des contributions aux logiciels libres.
2018
Riemannian classification for SSVEP based BCI: offline versus online implementations
Sylvain Chevallier, Emmanuel Kalunga, Quentin Barthélemy, and 1 more author
In Brain-Computer Interfaces Handbook, 2018
Brain-machine interface for mechanical ventilation using respiratory-related evoked potential
Sylvain Chevallier, Guillaume Bao, Mayssa Hammami, and 5 more authors
Muscle noises, line noises and eye movements are the main interferences that make difficulties when interpreting and analyzing electroencephalographic signals. Many methods have been proposed for artifacts removing from EEG measurements, and especially those arising from an ocular source. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been proposed to remove ocular artifacts from multichannel EEG. In contrast to this, we present a new algorithm for ocular artifacts removal from a single electroencephalographic channel recording. This method is based on a set of information on brain wave frequencies. Our results on EEG data, collected from healthy subjects, show that our algorithm can effectively detect and remove ocular artifacts in EEG recordings.
Using Riemannian geometry for SSVEP-based Brain Computer Interface
Time-frequency optimization for discrimination between imagination of right and left hand movements based on two bipolar electroencephalography channels
Yuan Yang, Sylvain Chevallier, Joe Wiart, and 1 more author
EURASIP Journal on Advances in Signal Processing, 2014
Spiking neurons seem to capture important characteristics of biological neurons with relatively simple models. Their main interest is their ability to code information as discrete events. We have investigated the possible contributions of these models to artficial vision. Due to vision constraints, particularly for processing time, we use simple spiking neural models. A spiking neuron network architecture is described for encoding and extracting saliencies, thanks to the discretization induced by spiking neurons. The saliency map is a combination, in space and temporal domain, of visual modality maps (contrasts, orientations and colors) for different spatial scales. We propose a neural filtering method for building the modality maps. This method implements a process, similar to convolution filtering, by gradual approximation : the more processing time is allowed to the algorithm, the better is the approximation. The architecture ranks the saliencies in the order of their interest, with the most important encoded first. Another spiking neuron network, inspired by dynamic neural fields, is attached on top of the architecture, allowing to select the most salient region and to focus on. Experimental results point out that the architecture is able to extract saliencies in a sequence of images, to select the most salient region and to focus on this region, in presence of noise or when the saliency moves.
2008
Covert attention with a spiking neural network,
Sylvain Chevallier and Philippe Tarroux
In International Conference on Computer Vision Systems (ICVS), 2008