Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?

Rimbert, Sébastien; Gayraud, Nathalie; Bougrain, Laurent; Clerc, Maureen; Fleck, Stéphanie

doi:10.3389/fnhum.2018.00529

Cited by 49 publications

(45 citation statements)

References 51 publications

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“…For the present study, we, cannot follow up on this possibility, given that we did not include an MI ability or severity questionnaire. It should, however, be noted that former studies found no (Rimbert et al, 2019) or rather moderate (Vuckovic and Osuagwu, 2013;Marchesotti et al, 2016) relations between subjective MI ability measures and BCI literacy.…”

Section: Study Limitationsmentioning

confidence: 70%

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Spychała

Debener

Bongartz

et al. 2020

Front. Hum. Neurosci.

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Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.

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Section: Study Limitationsmentioning

confidence: 70%

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Spychała

Debener

Bongartz

et al. 2020

Front. Hum. Neurosci.

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“…Indeed, especially in studies involving human beings, users and experimenters can have a notable influence on the results. As an example, Rimbert et al [29] have conducted an experiment whose goal was to confirm the effectiveness of the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS), to estimate the performance of a Motor Imagery (MI)-based BMI. Predicting a subject's ability to use a BMI is one of the major issues in the BMI domain.…”

Section: Examples Of Useful Negative Results In Bmimentioning

confidence: 99%

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

Lotte

Jeunet

Chavarriaga

et al. 2019

Brain-Computer Interfaces

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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 of this workshop are reported herein. First, we demonstrate why (valid) negative results are useful, and even necessary for BMIs. These results can be used to confirm or disprove current BMI knowledge, or to refine current theories. Second, we provide concrete examples of such useful negative results, including the limits in BMI-control for complete locked-in users and predictors of motor imagery BMI performances. Finally, we suggest levers to promote the diffusion of (valid) BMI negative results, e.g., promoting hypothesis-driven research using valid statistical tools, organizing special issues dedicated to BMI negative results, or convincing institutions and editors that negative results are valuable.

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“…A lack of association between objective and subjective quality of MI is also reported in recent Brain‐Computer Interface studies focused on MI. For example, Rimbert, Gayraud, Bougrain, Clerc, and Fleck (2018) showed no significant correlation between MI performance (measured by taking into account the average classification accuracy on EEG data between right‐hand MI and a rest period) and the scores obtained at the Motor Imagery Questionnaire Revised‐Second Edition (Rimbert et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Eyes wide shut: How visual cues affect brain patterns of simulated gait

Zapparoli

Seghezzi

Sacheli

et al. 2020

Human Brain Mapping

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In the last 20 years, motor imagery (MI) has been extensively used to train motor abilities in sport and in rehabilitation. However, MI procedures are not all alike as much as their potential beneficiaries. Here we assessed whether the addition of visual cues could make MI performance more comparable with explicit motor performance in gait tasks. With fMRI we also explored the neural correlates of these experimental manipulations. We did this in elderly subjects who are known to rely less on kinesthetic information while favoring visual strategies during motor performance. Contrary to expectations, we found that the temporal coupling between execution and imagery times, an index of the quality of MI, was less precise when participants were allowed to visually explore the environment. While the brain activation patterns of the gait motor circuits were very similar in both an open‐eyed and eye‐shut virtual walking MI task, these differed for a vast temporo‐occipito‐parietal additional activation for open‐eyed MI. Crucially, the higher was the activity in this posterior network, the less accurate was the MI performance with eyes open at a clinical test of gait. We conclude that both visually‐cued and internally‐cued MI are associated with the neurofunctional activation of a gait specific motor system. The less precise behavioral coupling between imagined and executed gait while keeping eyes open may be attributed to the processing load implied in visual monitoring and scanning of the environment. The implications of these observations for rehabilitation of gait with MI are discussed.

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Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?

Cited by 49 publications

References 51 publications

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Turning negative into positives! Exploiting ‘negative’ results in Brain–Machine Interface (BMI) research

Eyes wide shut: How visual cues affect brain patterns of simulated gait

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