Brain Training with Neurofeedback

Perronnet, Lorraine; Lécuyer, Anatole; Lotte, Fabien; Clerc, Maureen; Barillot, Christian

doi:10.1002/9781119144977.ch13

Cited by 10 publications

(7 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NF has been investigated for a wide range of clinical (Birbaumer et al, 2009) and non clinical applications (Sulzer et al, 2013;Thibault et al, 2015Thibault et al, , 2016Sitaram et al, 2016;Arns et al, 2017;Gruzelier, 2014). However its effective deployment in the clinical practice is being held back by the debated evidence about its efficiency, most likely as a result of poor study design and lack of established guidelines and knowledge about the underlying mechanisms of NF (Thibault et al, 2016;Perronnet et al, 2016). In recent years, increasingly rigorous approaches are becoming the new standard (Ros et al, 2019;Sulzer et al, 2013;Stoeckel et al, 2014;Thibault et al, 2016), and new studies are digging into the mechanisms Sitaram et al, 2016;Emmert et al, 2016;Birbaumer et al, 2013;Kober et al, 2013) as well as the methodological aspects of NF (Sorger et al, 2019;Emmert et al, 2017;Krause et al, 2017;Sorger et al, 2016;Sepulveda et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

Lécuyer

Mano

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain function by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, most commonly EEG or fMRI. EEG-fMRI-neurofeedback (EEG-fMRI-NF) is a new NF approach that consists of providing a NF based simultaneously on EEG and fMRI. By exploiting the complementarity of these two modalities, EEG-fMRI-NF opens a spectrum of possibilities for defining bimodal NF targets that could be more ro-bust, flexible and effective than unimodal ones. However facing a greater amount of information, the question arises of how to represent the EEG and fMRI features simultaneously in order to allow the subject to achieve better self-regulation. In this work, we propose that the EEG and fMRI features should be represented in a single bimodal feedback, which we refer to as integrated feedback. We then introduce two integrated feedback strategies for EEG-fMRI-NF and compare their early effects on a motor imagery task with a between-group design. The first group (BI DIM, n=10) was shown a two-dimensional (2D) feedback in which each dimension de-picted the information from one modality. The second group (UNI DIM, n=10) was shown a one-dimensional (1D) feedback that integrated both types of information even further by merging them into one. Online fMRI activations were significantly higher in the UNI DIM group than in the BI DIM group, which suggests that the 1D feedback is easier to control than the 2D feedback. However when looking at posthoc activation levels, the difference of fMRI activation levels between the NF runs and the preliminary motor imagery run without NF was more significant in the 2D group. Moreover, subjects from the BI DIM group produced more specific BOLD activations with a notably stronger activation in the right superior parietal lobule (BI DIM > UNI DIM, p < 0.001, uncorrected). These results suggest that the 2D feedback encourages subjects to explore their strategies to recruit more spe-cific brain patterns. To summarize, our study shows that 1D and 2D integrated feedbacks are effective but also appear to be complementary and could therefore be used in combination in a bimodal NF training program. Altogether, our study paves the way to novel integrated feedback strategies for the development of flexible and effective bimodal NF paradigms making the most of the bimodal information and better suited to clinical applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Perronnet

Lécuyer

Mano

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As NF, brain computer interfaces (BCI) also rely on a closed-loop that exploits brain activity in real-time. Traditional BCIs, so called assistive BCIs, are intended for the purpose of control and communication of an external object, while NF is intended to allow an individual to acquire control over him/herself (Wyckoff and Birbaumer, 2014 ; Perronnet et al, 2016 ). This distinction has tended to fade since the development of restorative/rehabilitative BCIs which target brain rehabilitation applications like NF (Soekadar et al, 2011 ; Chaudhary et al, 2016 ; Perronnet et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

“…Traditional BCIs, so called assistive BCIs, are intended for the purpose of control and communication of an external object, while NF is intended to allow an individual to acquire control over him/herself (Wyckoff and Birbaumer, 2014 ; Perronnet et al, 2016 ). This distinction has tended to fade since the development of restorative/rehabilitative BCIs which target brain rehabilitation applications like NF (Soekadar et al, 2011 ; Chaudhary et al, 2016 ; Perronnet et al, 2016 ). In the BCI community, the field of hybrid BCI has recently emerged (Pfurtscheller et al, 2010 ; Amiri et al, 2013 ), the term “hybrid” referring to a multimodal combination of sensors.…”

Section: Introductionmentioning

confidence: 99%

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Perronnet

Lécuyer

Mano

et al. 2017

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

show abstract

“…In this case, the BCI system generated/trained by the data in a specific period may not adapt to the same user at a different time, raising a significant issue for the long-time EEG device that might require frequent updates. At last, subject dependence is another issue of EEG [ 28 ]. This phenomenon appears because of the psychological and physiological variations between people, which results in the difference of voltage magnitude in EEG signals across individuals that significantly affect the BCI system’s generality.…”

Section: Introductionmentioning

confidence: 99%

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Huang

Liang

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.

show abstract

Brain Training with Neurofeedback

Cited by 10 publications

References 64 publications

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review

Contact Info

Product

Resources

About