Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces

We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-invasive BCIs

A Database Management System For Vision Applications

The Manchester Content Addressable Image Database is ageneric tool which has been designed for image informatics and computer vision problems. The system stores pre-compuied feature tokens, which are obtained by conventional processing of the input images, into a database which is accessed by a specialised query language (M VQL [1]). The M VQL is based on the creation and refinement of groups of features by computing attributes. We illustrate the system with an application concerned with the detection and classification of microfossil images. We use the MVQL to express a projective circular Hough Transform for microfossil detection. We also address the problem of classifying detected structures into six broad morphological groups. This is achieved using MVQL to define "structure " measures from the distribution of curve tokens in a circular region around each microfossil. 1. Introduction. The aim of this paper is to present a simple database management system (dbms), which includes a data entry system, query interface and some graphical capability, which has been specifically designed for vision research. The work is particularly motivated b

Intentional binding enhances hybrid BCI control

Mental imagery-based brain-computer interfaces (BCIs) allow to interact with the external environment by naturally bypassing the musculoskeletal system. Making BCIs efficient and accurate is paramount to improve the reliability of real-life and clinical applications, from open-loop device control to closed-loop neurorehabilitation. By promoting sense of agency and embodiment, realistic setups including multimodal channels of communication, such as eye-gaze, and robotic prostheses aim to improve BCI performance. However, how the mental imagery command should be integrated in those hybrid systems so as to ensure the best interaction is still poorly understood. To address this question, we performed a hybrid EEG-based BCI experiment involving healthy volunteers enrolled in a reach-and-grasp action operated by a robotic arm. Main results showed that the hand grasping motor imagery timing significantly affects the BCI accuracy as well as the spatiotemporal brain dynamics. Higher control accuracy was obtained when motor imagery is performed just after the robot reaching, as compared to before or during the movement. The proximity with the subsequent robot grasping favored intentional binding, led to stronger motor-related brain activity, and primed the ability of sensorimotor areas to integrate information from regions implicated in higher-order cognitive functions. Taken together, these findings provided fresh evidence about the effects of intentional binding on human behavior and cortical network dynamics that can be exploited to design a new generation of efficient brain-machine interfaces.Comment: 18 pages, 5 figures, 7 supplementary material

Exploring strategies for multimodal BCIs in an enriched environment

International audienceBrain computer interfaces rely on cognitive tasks easy at first sight but that reveal to be complex to perform. In this context, providing engaging feedback and subject's embodiment is one of the keys for the overall system performance. However, noninvasive brain activity alone has been demonstrated to be often insufficient to precisely control all the degrees of freedom of complex external devices such as a robotic arm. Here, we developed a hybrid BCI that also integrates eye-tracking technology to improve the overall sense of agency of the subject. While this solution has been explored before, the best strategy on how to combine gaze and brain activity to obtain effective results has been poorly studied. To address this gap, we explore two different strategies where the timing to perform motor imagery changes; one strategy could be less intuitive compared to the other and this would result in differences of performance

Comparison of strategies to elicit motor imagery-related brain patterns in multimodal BCI settings

International audienceCognitive tasks such as motor imagery (MI) used in Brain Machine Interface (BMI) present many issues: they are demanding, often counter-intuitive, and complex to describe to the subject during the instruction. Engaging feedback related to brain activity are key to maintain the subject involved in the task. We build a framework where the subject controls a robotic arm both by gaze and brain activity in an enriched environment using eye tracking glasses and electro-encephalography (EEG). In our study, we tackle the important question of the preferable moment to perform the MI task in the context of the robotic arm control. To answer this question, we design a protocol where subjects are placed in front of the robotic arm and choose with gaze which object to seize. Then based on stimuli blended in an augmented table, the subjects perform MI or resting state tasks. The stimuli consist of a red (MI) or blue (Resting) dot circling the object to seize. At the end of a MI task, the hand should close. There are three strategies corresponding to three different moments when to perform the mental task, 1) After the robot's movement towards the object, 2) Before the robot's movement, 3) Meanwhile the robot's movement. The experimentation is split into a calibration and two control phases, in the calibration phase, the hand always close during MI task, and in the control phases, it relies on the subject's brain activity. We rely on power spectral density estimate using Burg Auto regressive method to differentiate between MI and resting state in the alpha and beta bands (8-35 Hz). Our method to compare the strategies relies on classification performance (LDA 2 classes) using sensitivity, and statistical differences between conditions (R-squared map). The early results on the first 10 subjects show significant differences between strategy 1 and 2 for offline classification analysis and a trend on the real performance scores in favor of the strategy one. We observed in all subjects' brain activity localized in the motor cortex at significant level with regards to resting state. This indicates that the framework placing the subjects at the center with high sense of agency reinforced by gaze control is giving good results and allows to be more certain that the subject is doing the right task. Taken together, our results indicate that investigating the moment when to perform MI in the framework is a relevant parameter. The strategy where the robot is at the object level when MI is performed seems so far to be the best strategy

Analyse de sensibilité à des changements morphologiques du complexe de l’épaule : application aux gestes de percussion au cours de débitage oldowayen

Si la fabrication et l’utilisation d’outils lithiques ont incontestablement joué un rôle déterminant dans l’évolution des hominines, l’impact de tels comportements sur leur morphologie semble moins faire consensus. Toutefois, il semble que l’architecture et les proportions du complexe de l’épaule chez les premiers représentants du genre Homo aient pu avoir été contraintes par ces comportements. Afin de discuter des potentiels avantages adaptatifs de ces traits morphologiques dans le cadre de ..

M/EEG networks integration to elicit patterns of motor imagery-based BCI training

International audienceNon-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users [1]. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in multimodal network recruitment. We hypothesized that integrating information from EEG and MEG data, show a better description of the core-periphery changes occurring during a motor imagery-based BCI training. Such an enriched description could reveal fresh insights into learning processes that are difficult to observe at the signle layer level and eventually improve the prediction of future BCI performance.multimodal brain network properties could be considered as a potential marker of BCI learning

M/EEG networks integration to elicit patters of motor imagery-based Brain-Computer Interface (BCI) training

International audienceNon-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users [1]. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in multimodal network recruitment. We hypothesized that integrating information from EEG and MEG data, show a better description of the core-periphery changes occurring during a motor imagery-based BCI training. Such an enriched description could reveal fresh insights into learning processes that are difficult to observe at the signle layer level and eventually improve the prediction of future BCI performance.multimodal brain network properties could be considered as a potential marker of BCI learning

Exploiting brain critical dynamics to inform Brain-Computer Interfaces performance

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Functional connectivity predicts MI-based BCI learning

International audienceNon-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in terms of cortical activations and network recruitment. We hypothesized that the associated characteristics would be able to predict the success of learning