A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

Lioi, Giulia; Butet, Simon; Fleury, Mathis; Bannier, Élise; Lécuyer, Anatole; Barillot, Christian

doi:10.3389/fnhum.2020.00037

Cited by 45 publications

(51 citation statements)

References 44 publications

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“…Haptic-based MI BCIs are promising for functional rehabilitation of stroke patients, as such training can also be used with patients having no residual movement. The aim of such BCI/NF systems is to stimulate neural plasticity in perilesional brain motor areas and support upper limb functional improvement (Lioi et al, 2018(Lioi et al, , 2020. Since haptic BCI/NF-based SMR techniques achieve motor imagery with concurrent motor learning via kinesthetic feedback, it is natural to think of rehabilitation of stroke patients even in a chronic condition.…”

Section: Motor Imagery Paradigmsmentioning

confidence: 99%

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

et al. 2020

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Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control of external devices. These approaches have historically employed visual stimuli. However, in some cases vision is unsuitable or inadequately engaging. Other sensory modalities, such as auditory or haptic feedback have been explored, and multisensory stimulation is expected to improve the quality of the interaction loop. Moreover, for motor imagery tasks, closing the sensorimotor loop through haptic feedback may be relevant for motor rehabilitation applications, as it can promote plasticity mechanisms. This survey reviews the various haptic technologies and describes their application to BCIs and NF. We identify major trends in the use of haptic interfaces for BCI and NF systems and discuss crucial aspects that could motivate further studies.

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Section: Motor Imagery Paradigmsmentioning

confidence: 99%

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

et al. 2020

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“…While the 2D strategy could prove valuable in the longer term to reach more specific self-regulation, the 1D metaphor could be well suited during earlier phases of a NF program or for clinical application as it is easier to control. This is also the reason why we opted for the 1D metaphor in a pilot study where we tested the feasibility of EEG-fMRI NF for stroke rehabilitation (Lioi et al, 2020).…”

Section: Online and Posthoc Performancementioning

confidence: 99%

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

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Lécuyer

Mano

et al. 2018

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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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“…Pilot studies using real-time fMRI-based neurofeedback for motor function recovery in stroke patients have also shown some promise (Sitaram et al, 2012;Liew et al, 2016). Using motor-imagery-based strategies, patients were able to increase connectivity between cortical and subcortical regions (Liew et al, 2016) and increase regulation of the activity in regions relevant to motor function (Sitaram et al, 2012;Lioi et al, 2020). An adaptive, multi-target motor imagery training approach was also proposed by Lioi et al (2020).…”

Section: Discussionmentioning

confidence: 99%

Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training

et al. 2020

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In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application.

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A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients

Cited by 45 publications

References 44 publications

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback

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

Improving Real-Time Brain State Classification of Motor Imagery Tasks During Neurofeedback Training

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