Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

Jeunet, Camille; Lotte, Fabien; Batail, Jean-Marie; Philip, Pierre; Franchi, Jean-Arthur Micoulaud

doi:10.1016/j.neuroscience.2018.03.013

Cited by 47 publications

(27 citation statements)

References 81 publications

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“…This issue is currently being investigating. Notably, a lot of effort is dedicated to better understand the factors (e.g., users' states and profile) influencing one's responsiveness to NF in order then to design procedures adapted to each user/athlete 42 . Finally, beyond this aspect of NF efficacy, an exciting challenge (in order to achieve high proficiency) will consist in integrating this procedure in a coherent and motivating global training strategy which enables an efficient transfer of skills on the field, possibly using virtual reality.…”

Section: Discussionmentioning

confidence: 99%

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Jeunet

Tonin

Albert³

et al. 2020

Sci Rep

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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 selfregulate specific brain rhythms and consequently improve their performances. Here, we focused on soccer goalkeepers' Covert Visual Spatial Attention (CVSA) abilities, which are essential for these athletes to reach high performances. We looked for Electroencephalography (EEG) markers of CVSA usable for virtual reality-based NF training procedures, i.e., markers that comply with the following criteria: (1) specific to CVSA, (2) detectable in real-time and (3) related to goalkeepers' performance/ expertise. Our results revealed that the best-known EEG marker of CVSA-increased α-power ipsilateral to the attended hemi-field-was not usable since it did not comply with criteria 2 and 3. Nonetheless, we highlighted a significant positive correlation between athletes' improvement in CVSA abilities and the increase of their α-power at rest. While the specificity of this marker remains to be demonstrated, it complied with both criteria 2 and 3. This result suggests that it may be possible to design innovative ecological training procedures for goalkeepers, for instance using a combination of NF and cognitive tasks performed in virtual reality.

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Section: Discussionmentioning

confidence: 99%

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Jeunet

Tonin

Albert³

et al. 2020

Sci Rep

Self Cite

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“…The impact of potential side effects of social aspects in BCI and NF applications remains to be investigated. Such consideration would be in line with recent developments in the field, i.e., focus towards user-centered [40] and individualized design [41] of MI NF/BCI implementations.…”

Section: Comparison Of Relative Erd In Single and Competitive Multi-umentioning

confidence: 93%

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

Daeglau

Wallhoff

Debener

et al. 2020

Sensors

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Optimizing neurofeedback (NF) and brain–computer interface (BCI) implementations constitutes a challenge across many fields and has so far been addressed by, among others, advancing signal processing methods or predicting the user’s control ability from neurophysiological or psychological measures. In comparison, how context factors influence NF/BCI performance is largely unexplored. We here investigate whether a competitive multi-user condition leads to better NF/BCI performance than a single-user condition. We implemented a foot motor imagery (MI) NF with mobile electroencephalography (EEG). Twenty-five healthy, young participants steered a humanoid robot in a single-user condition and in a competitive multi-user race condition using a second humanoid robot and a pseudo competitor. NF was based on 8–30 Hz relative event-related desynchronization (ERD) over sensorimotor areas. There was no significant difference between the ERD during the competitive multi-user condition and the single-user condition but considerable inter-individual differences regarding which condition yielded a stronger ERD. Notably, the stronger condition could be predicted from the participants’ MI-induced ERD obtained before the NF blocks. Our findings may contribute to enhance the performance of NF/BCI implementations and highlight the necessity of individualizing context factors.

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“…Accurate assessments of strategies, motivation, cognitive state but also of personality traits (Jeunet et al, 2015a(Jeunet et al, , 2018 are key to interpret the learning curve and the effects of NF training. Our questionnaire inquiring about strategies, fatigue, motivation, perceived performance, and confidence in the feedback gave us valuable qualitative knowledge about how subjects in the BiDim group reacted to the complexity of the feedback.…”

Section: Group Distribution Across the 3 Nf Runsmentioning

confidence: 99%

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

Perronnet

Lécuyer

Mano

et al. 2018

Preprint

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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.

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Using Recent BCI Literature to Deepen our Understanding of Clinical Neurofeedback: A Short Review

Cited by 47 publications

References 81 publications

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Uncovering EEG Correlates of Covert Attention in Soccer Goalkeepers: Towards Innovative Sport Training Procedures

Challenge Accepted? Individual Performance Gains for Motor Imagery Practice with Humanoid Robotic EEG Neurofeedback

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

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