Camille Jeunet

French National Centre for Scientific Research | CNRS

Objective. Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) of a participant’s brain activity that they must learn to self-regulate. A classical visual FB delivered in a NF task is a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked—from the subject's persp...

Neurofeedback (NF) consists in training the self-regulation of some target neural activity. Yet, the neural underpinnings of NF performance remains largely unknown. Here, we investigated Motor Imagery (MI) based NF with EEG, training subjects to regulate motor-related activity in the large β (8-30 Hz) band. We examined the electrophysiological corr...

Objective. Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) on the participant's brain activity that they must learn to self-regulate. The visual FB traditionally delivered in a NF task manifests as a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked-from t...

The sense of embodiment refers to the sensations of being inside, having, and controlling a body. In virtual reality, it is possible to substitute a person’s body with a virtual body, referred to as an avatar. Modulations of the sense of embodiment through modifications of this avatar have perceptual and behavioural consequences on users that can i...

Addiction is characterized by a loss of control over use of reinforcers such as substances (alcohol, tobacco, cannabis...). Craving is a clinical phenomenon defined as a strong urge to use and plays a central role in addiction. Craving is a dynamic phenomenon that fluctuates in intensity and frequency and the daily variations of which are prospecti...

Motor Imagery Neurofeedback (MI-NF) trains participants to voluntary reduce sensorimotor (SMR) activity by imagining movements. Feedback (FB) is central to NF learning but most studies feature abstract FB, which obscures the causal link between MI and FB. Perceiving this link is crucial to feel control, i.e: agency over feedback movements. We focus...

[This corrects the article DOI: 10.1371/journal.pone.0143962.].

Introduction Strokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expecte...

Up to 30% of Motor Imagery-based BCI (MI-BCI) users do not achieve reliable BCI control. Operating a BCI system is complex: the training procedure is crucial. Feedback given is often abstract. Does feedback transparency impact NF performance? Transparency may reinforce sense of agency over the feedback, easing kinesthetic MI. This could facilitate...

This Research Topic is composed of 11 accepted papers: seven dedicated to original research, a perspective, a mini review and two opinion pieces, and are dedicated to various themes and perspectives. These contributions address the multi-faceted nature of non-clinical BCIs, ranging from ethical ramifications of these neurotechnologies, applications...

Motor Imagery-based BCI (MI-BCI) performances remain highly variable, with up to 30% of users unable to achieve control. Operating a BCI system requires users to learn to modulate their neural activity. This is a complex task, thus the training procedure is crucial. Our study draws on task-feedback transparency to improve the feedback given during...

Motor imagery-based brain–computer interfaces (MI-BCIs) rely on interactions between humans and machines. The (learning) characteristics of both components are key to understand and improve performances. Data-driven methods are often used to select/extract features with very little neurophysiological prior. Should they include prior knowledge and w...

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because current methods cannot generalise well across human subjects' data and handle learning from different heterogeneously collected data sets, thus limiting the scale of...

Purpose of Review This review provides an overview of current knowledge and understanding of EEG neurofeedback for anxiety disorders and post-traumatic stress disorders. Recent Findings The manifestations of anxiety disorders and post-traumatic stress disorders (PTSD) are associated with dysfunctions of neurophysiological stress axes and brain aro...

Introduction Motor Imagery (MI) is a powerful tool to stimulate sensorimotor brain areas and is currently used in motor rehabilitation after a stroke. The aim of our study was to evaluate whether an illusion of movement induced by visuo-proprioceptive immersion (VPI) including tendon vibration (TV) and Virtual moving hand (VR) combined with MI task...

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g.,...

These authors contributed equally to this work. All other authors are listed in reverse alphabetical order. Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards...

Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the abil...

The neuronal loss resulting from stroke forces 80% of the patients to undergo motor rehabilitation, for which Brain-Computer Interfaces (BCIs) and NeuroFeedback (NF) can be used. During the rehabilitation, when patients attempt or imagine performing a movement, BCIs/NF provide them with a synchronized sensory (e.g., tactile) feedback based on their...

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Advances in sports sciences and neurosciences offer new opportunities to design efficient and motivating sport training tools. For instance, using NeuroFeedback (NF), athletes can learn to self-regulate specific brain rhythms and consequently improve their performances. Here, we focused on soccer goalkeepers’ Covert Visual Spatial Attention (CVSA)...

Mental-Imagery based Brain-Computer Interfaces (MI-BCI) present new opportunities to interact with digital technologies, such as wheelchairs or neuroprostheses, only by performing mental imagery tasks (e.g., imagining an object rotating or imagining hand movements). MI-BCIs can also be used for several applications such as communication or post-str...

Results that do not confirm expectations are generally referred to as ‘negative’ results. While essential for scientific progress, they are too rarely reported in the literature – Brain–Machine Interface (BMI) research is no exception. This led us to organize a workshop on BMI negative results during the 2018 International BCI meeting. The outcomes...

Contexte L’entraînement neurofeedback consiste à apprendre à des patients à moduler volontairement des rythmes d’activité cérébrale spécifiques, sous-tendant des capacités cognitives/motrices d’intérêt. Le neurofeedback pourrait être un outil de remédiation cognitive prometteur. Cependant, actuellement, une large proportion de patients semble ne pa...

Mental Imagery based Brain-Computer Interfaces (MI-BCI) enable their users to control an interface, e.g., a prosthesis, by performing mental imagery tasks only, such as imagining a right arm movement while their brain activity is measured and processed by the system. Designing and using a BCI requires users to learn how to produce different and sta...

The clinical efficacy of neurofeedback is still a matter of debate. This paper analyzes the factors that should be taken into account in a transdisciplinary approach to evaluate the use of EEG NFB as a therapeutic tool in psychiatry. Neurofeedback is a neurocognitive therapy based on human–computer interaction that enables subjects to train volunta...

This checklist is intended to encourage robust experimental design and clear reporting for clinical and cognitive-behavioural neurofeedback experiments.

This checklist is intended to encourage robust experimental design and clear reporting for clinical and cognitive-behavioural neurofeedback experiments. Available at https://psyarxiv.com/nyx84

Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedbac...

Many Brain Computer Interface (BCI) and neurofeedback studies have investigated the impact of sensorimotor rhythm (SMR) self-regulation training procedures on motor skills enhancement in healthy subjects and patients with motor disabilities. This critical review aims first to introduce the different definitions of SMR EEG target in BCI/Neurofeedbac...

Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs). E...

Objective: While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires the use of appropriate reliability metrics to quantify both the classification...

In their recent paper, Alkoby et al. (2017) provide the readership with an extensive and very insightful review of the factors influencing NeuroFeedback (NF) performance. These factors are drawn from both the NF literature and the Brain-Computer Interface (BCI) literature. Our short review aims to complement Alkoby et al.'s review by reporting rece...

While the Sense of Agency (SoA) has so far been predominantly characterised in VR as a component of the Sense of Embodiment, other communities (e.g., in psychology or neurosciences) have investigated the SoA from a different perspective proposing complementary theories. Yet, despite the acknowledged potential benefits of catching up with these theo...

Le neurofeedback consiste à mesurer une activité électrophysiologique corticale, à traiter le signal au moyen d’une interface technique afin d’en extraire un paramètre d’intérêt, puis à le présenter en temps réel au sujet sous la forme d’une information compréhensible [1]. L’objectif est d’apprendre au sujet à moduler son activité corticale, en tem...

Brain-Computer Interfaces (BCIs) have brought new, exciting and promising perspectives of interaction. On the one hand, active BCIs enable users to control applications (such as assistive technologies or video games) using their brain activity alone. On the other hand, passive BCIs bring the possibility of adapting an application/interface based on...

Mental-imagery based brain-computer interfaces (MI-BCIs) enable users to interact with theirenvironment using their brain-activity alone, by performing mental-imagery tasks. This thesisaims to contribute to the improvement of MI-BCIs in order to render them more usable. MIBCIsare bringing innovative prospects in many fields, ranging from stroke reh...

This chapter gives an idea of the current state of research of Brain-Computer Interfaces (BCI) learning protocols. The BCI community now recognizes that in order to achieve an improvement in performance, the user must be included in the loop, and so learning protocols must be improved accordingly. It have also shown that by building on theories in...

While being very promising for a wide range of applications, mental-imagery-based brain–computer interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10–30% of users are unable to control MI-BCIs (so-called BCI illiteracy) while only a small proportion...

While Mental Imagery based BCIs (MI-BCIs) are promising for many applications, their usability " out-of-the-lab " has been questioned due to their lack of reliability: literature reports that 15% to 30% of users cannot control such a technology, while most of the remaining users obtain only modest performances [1]. Standard MI-BCI training protocol...

Despite their promising potential impact for many applications, Mental-Imagery based BCIs (MI-BCIs) remain barely used outside laboratories. One reason is that 15% to 30% of naïve users seem unable to control them [1] and only a few reach high control abilities. Although different predictors of BCI performance (i.e., command classification accuracy...

Objective: While promising, electroencephaloraphy based brain-computer interfaces (BCIs) are barely used due to their lack of reliability: 15% to 30% of users are unable to control a BCI. Standard training protocols may be partly responsible as they do not satisfy recommendations from psychology. Our main objective was to determine in practice to...

While being very promising for a wide range of applications, Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10 to 30% of users are unable to control MI-BCIs (so-called " BCI illiteracy ") while only a small pro...

Motor-Imagery based Brain Computer Interfaces (MI-BCIs) allow users to interact with computers by imagining limb movements. MI-BCIs are very promising for a wide range of applications as they offer a new and non-time locked modality of control. However, most MI-BCIs involve visual feedback to inform the user about the system’s decisions, which make...

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty en...

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain activity alone (typically measured by ElectroEn-cephaloGraphy-EEG), which is processed while they perform specific mental tasks. While very promising MI-BCIs remain barely used outside laboratories because of the difficulty en...

Although EEG-based BCI are very promising for numerous applications, they mostly remain prototypes not used outside laboratories, due to their low reliability. Poor BCI performances are partly due to imperfect EEG signal processing algorithms but also to the user, who may not be able to produce reliable EEG patterns. This paper presents some of our...

Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive tool to adapt the human–computer interaction to the user state. To create systems that reliably work in the complexity of the real world, a robustness against contextual changes (e.g., mood), has to be achieved. To study the resilience of state-of-the-art EEG-b...

Stress is a major societal issue with negative impacts on health and economy. Physiological computing offers a continuous, direct, and unobtrusive method for stress level assessment and computer-assisted stress management. However, stress is a complex construct and its physiology can vary depending on its source: cognitive workload or social evalua...

Stress is a major societal issue with negative impacts on health and economy. Physiological computing offers a continuous, direct, and unobtrusive method for stress level assessment and computer-assisted stress management. However, stress is a complex construct and its physiology can vary depending on its source: cognitive workload or social evalua...