PAPADOPOULO , Théodore

Electro- and Magneto- encephalography (EEG and MEG) are measuring non-invasively the post-synaptic brain electrical activity. At each time instant, EEG and MEG provide a few hundreds ofmeasurements at the scalp (or near) level, and these measurements can be obtained at about amillisecond time resolution. In the frequency domain of interest for...

Objective: BCI (Brain-Computer Interface) technology operates in three modes: online, offline, and pseudo-online. In the online mode, real-time EEG data is constantly analyzed. In offline mode, the signal is acquired and processed afterwards. The pseudo-online mode processes collected data as if they were received in real-time. The main...

Introduction:Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two noninvasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering...

Over the past 30 years, neuroimaging has become a predominant technique. One might envision that over the next years it will play a major role in disclosing the brain's functional interactions. In this work, we use information coming from diffusion magnetic resonance imaging (dMRI) to reconstruct effective brain network from two functional...

Introduction: A BCI technology can operate in 3 different modalities: online mode which requires analyzingthe new real-time EEG data while acquiring it, offline mode where data is acquired and saved to a file and thenanalyzed afterwards (giving access to the data as a whole) and pseudo-online mode, which is a mix betweenthe previous two modes,...

Understanding how brain regions interact to perform a specific task is very challenging. EEG and MEG are two non-invasive imaging modalities that allow the measurement of brain activation with high temporal resolution. Several works in EEG/MEG source reconstruction show that estimating brain activation can be improved by considering...

Objective: Electroencephalography signals are recorded as a multidimensional dataset. We propose a new framework based on the augmented covariance extracted from an autoregressive model to improve motor imagery classification. Methods: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a...

The Electroencephalographiy (EEG) and Magnetoencephalography (MEG) are two non-invasive imaging modalities that measures the brain activity. Obtaining the brain activity with the distributed source model from these measurements is an ill-posed problem due to the high number of unknowns compared to the number of measurements. A unique solution...

In this paper, we present a new approach to reconstruct dipole magnitudes of a distributed source model for magnetoencephalographic (MEG) and electroencephalographic (EEG). This approach is based on the structural homogeneity of the cortical regions which are obtained using diffusion MRI (dMRI). First, we parcellate the cortical surface into...

Electroencephalography-based brain-computer interface (EEG-BCI) systems have been developed to enable individuals with physical disabilities to control external devices using their thoughts. Motor imagery (MI) is one of the most common paradigms used in EEG-BCI, where users are instructed to imagine performing a specific motor task, such as...

Objective: Electroencephalography signals are recorded as a multidimensional dataset. We propose a new framework based on the augmented covariance extracted from an autoregressive model to improve motor imagery classification. Methods: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a...

Non-iterative two-stage approaches have been used to estimate source interactions. They first reconstruct sources and then compute the MAR model for the localized sources. They showed good results when working in high signal-to-noise ratio (SNR) settings, but fail in detecting the true interactions when working in low SNR. Our framework is...

Ce chapitre illustre, au travers d'une application de clavier virtuel P300, plusieurs notions fondamentales des BCI : prétraitement, extraction de caractéristiques, classification et apprentissage.Sans revenir sur les détails de ces notions, puisqu'elles ont déjà été abordées dans la première partie de cet ouvrage, ce chapitre propose au...

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Electroencephalography(EEG) and magnetoencephalography (MEG) measure the electrical activity of the functioning brain usinga set of sensors placed on the scalp (electrodes and magnetometers). Magneto- or electroencephalography (M/EEG) have the same biological origin, the activity of the pyramidal neurones within the cortex. The signals obtained...

In a Mental Imagery Brain-Computer Interface the user has to perform a specific mental task that generates electroencephalography (EEG) components, which can be translated in commands to control a BCI system. The development of a high-performance MI-BCI requires a long training, lasting several weeks or months, in order to improve the ability...

In this paper, we present a method that aims at parcellating the cortical surface from individual anatomy. The parcellation is obtained using the Mutual Nearest Neighbor (MNN) criterion to obtain regions with similar structural connectivity. The structural connectivity is obtained by applying a probabilis-tic tractography on the diffusion MRI...

The M/EEG inverse problem is ill-posed. Thus additional hypotheses are needed to constrain the solution space. In this work, we consider that brain activity which generates an M/EEG signal is a connected cortical region. We study the case when only one region is active at once. We show that even in this simple case several configurations can...

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivity of the different head tissues. Conductivity values are subject dependent, so non-invasive methods for conductivity estimation are necessary to fine tune the EEG models. To do so, the EEG forward problem solution...

In this paper, we present a framework to reconstruct spatially localized sources from Magnetoencephalogra-phy (MEG)/Electroencephalography (EEG) using spatiotempo-ral constraint. The source dynamics are represented by a Mul-tivariate Autoregressive (MAR) model whose matrix elements are constrained by the anatomical connectivity obtained from...

Epilepsy is a neurological disorder that manifests itself as episodes od epileptic seizure characterized by an unusually sporadic neural activity observable by EEG. A model of transgenic mouse affected by epilepsy has been developed in order to better understand, predict and preventively treat these seizures. Between the seizures, we observe...

Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e.g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a...

Signals obtained from magneto- or electroencephalography (M/EEG) are very noisy and inherently multi-dimensional, i.e. provide a vector of measurements at each single time instant. To cope with noise, researchers traditionally acquire measurements over multiple repetitions (trials) and average them to classify various patterns of activity. This...

This chapter illustrates by means of a P300 virtual keyboard application, several fundamental notions of brain–computer interfaces (BCIs): preprocessing, extraction of characteristics, classification and training. It proposes to the reader to appropriate them with a specific application. The P300 virtual keyboard application flashes the symbols...

Electroencephalogram (EEG) signal is recorded as a multidimensional dataset, which can be interpreted using autoregressive (AR) models. In this way, it is possible to extract relevant information about the signal, which can be used for Brain Computer Inteface (BCI) classification. Yule-Walker equations can be derived from the AR equation, which...