Brain-computer interfaces
and EEG signal processing
Despite its short history, the use of Riemannian geometry in brain-computer interface (BCI) decoding is currently attracting increasing attention, due to accumulating documentation of its simplicity, accuracy,...
Estimating means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD) matrices has proved of great utility in applications requiring interpolation, extrapolation, smoothing, signal...
The objective of this study was to determine whether kinematic data collected by the Microsoft Kinect 2 (MK2) could be used to quantify postural stability in healthy...
This study presents the outcome of the 5-year-long French national project aiming at the development and evaluation of an effective brain-computer interface (BCI) prototype for the communication...
The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could...
We explore the connection between two problems that have arisen independently in the signal processing and related fields: the estimation of the geometric mean of a set...
The use of spatial covariance matrix as a feature is investigated for motor imagery EEG-based classification in brain–computer interface applications. A new kernel is derived by establishing...
This paper presents a new classification framework for brain-computer interface (BCI) based on motor imagery. This framework involves the concept of Riemannian geometry in the manifold of...
Riemannian geometry has been found accurate and robust for classifying multidimensional data, for instance, in brain-computer interfaces based on electroencephalography. Given a number of data points on...
The recognition of brain evoked responses at the single-trial level is a challenging task. Typical non-invasive brain-computer interfaces based on event-related brain responses use eletroencephalograhy. In this study, we consider...
The estimation of means of data points lying on the Riemannian manifold of symmetric positive-definite (SPD) matrices is of great utility in classification problems and is currently...
To better understand the relationship between EEG signals and hand movements the WAY Consortium has organized the Grasp-and-Lift EEG Detection challenge. It was held in 2015 from...
The combined analyses of brain and muscle signals can be very useful in assessing the cortico-muscular connectivity and reorganization in brain injury. A basic approach would be...
In 2011 we proposed Brain Invaders , a BCI videogame inspired from the vintage game Space Invaders. The software was released open source and was compatible with OpenVIBE. The system...
Currently the Riemannian geometry of symmetric positive definite (SPD) matrices is gaining momentum as a powerful tool in a wide range of engineering applications such as image,...
Artifacts management is a critical problem in any applications involving on-line processing of EEG signals. This paper presents a multivariate automatic and adaptive method for identifying artifacts...
The use of spatial covariance matrix as feature is investigated for motor imagery EEG-based classification. A new kernel is derived by establishing a connection with the Riemannian...
This paper addresses the issue of asynchronous brain-switch. The detection of a specific brain pattern from the ongoing EEG activity is achieved by using the Riemannian geometry,...
This article describes a new algorithm to select a subset of electrodes in BCI experiments. It is illustrated on a two-class motor imagery paradigm. The proposed approach is based on...
This paper presents a link between the well known Common Spatial Pattern (CSP) algorithm and Riemannian geometry in the context of Brain Computer Interface (BCI). It will...
In brain-computer interfaces based on motor imagery, covariance matrices are widely used through spatial filters computation and other signal processing methods. Covariance matrices lie in the space...
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...
This chapter aims at providing the reader with two examples of open-source BCI-games that work with the OpenViBE platform. These two games are “Brain Invaders” and “Use-The-Force!” and are representative...
This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use...
Solution of the DecMeg 2014 challenge.
Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not...
Cette thèse a pour but le développement d’une Interface cerveau-machine (ICM) à partir de la mesure EEG,permettant à l’utilisateur de communiquer avec un dispositif externe directement par l’intermédiaire de son...
A classification method for classifying neural signals, the method comprising the following steps: