Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

Pillette, Léa; N’Kaoua, Bernard; Sabau, Romain; Glize, Bertrand; Lotte, Fabien

doi:10.3390/mti5030012

Cited by 17 publications

(15 citation statements)

References 54 publications

(115 reference statements)

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“…the next trial (Carabalona and Castiglioni, 2009); for example, motivation is impacted by feedback (Alimardani et al, 2014;Pillette et al, 2021). Despite the inter-trial interval that was randomized between 0.5 and 2.5 s, this effect might still be prevalent.…”

Section: The Impact Of Resting-state Connectivitymentioning

confidence: 99%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

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Section: The Impact Of Resting-state Connectivitymentioning

confidence: 99%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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“…Some studies made a point about the reliability of these tools. Pillette’s study did not find any influence of the initial KVIQ scores on MI performances ( Pillette et al, 2021 ). Concerning MIQ-RS, no significant correlation was found between the scores and the BCI performance in healthy subjects ( Rimbert et al, 2019 ).…”

Section: Methodsmentioning

confidence: 93%

“…Future perspectives may combine other types of feedback. An example can be visual feedback with a vibrotactile one, as in Leeb et al (2013) , Jeunet et al (2015b) , and Pillette et al (2021) , yet, it has to be tested in the stroke population. This type of feedback has proven to liberate the visual channel so that subjects can pay attention to other events such as distractors ( Leeb et al, 2013 ; Jeunet et al, 2015b ).…”

Section: Methodsmentioning

confidence: 99%

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Franc

Altamira

Guillen

et al. 2022

Front. Hum. Neurosci.

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Stroke is a severe health issue, and motor recovery after stroke remains an important challenge in the rehabilitation field. Neurofeedback (NFB), as part of a brain–computer interface, is a technique for modulating brain activity using on-line feedback that has proved to be useful in motor rehabilitation for the chronic stroke population in addition to traditional therapies. Nevertheless, its use and applications in the field still leave unresolved questions. The brain pathophysiological mechanisms after stroke remain partly unknown, and the possibilities for intervention on these mechanisms to promote cerebral plasticity are limited in clinical practice. In NFB motor rehabilitation, the aim is to adapt the therapy to the patient’s clinical context using brain imaging, considering the time after stroke, the localization of brain lesions, and their clinical impact, while taking into account currently used biomarkers and technical limitations. These modern techniques also allow a better understanding of the physiopathology and neuroplasticity of the brain after stroke. We conducted a narrative literature review of studies using NFB for post-stroke motor rehabilitation. The main goal was to decompose all the elements that can be modified in NFB therapies, which can lead to their adaptation according to the patient’s context and according to the current technological limits. Adaptation and individualization of care could derive from this analysis to better meet the patients’ needs. We focused on and highlighted the various clinical and technological components considering the most recent experiments. The second goal was to propose general recommendations and enhance the limits and perspectives to improve our general knowledge in the field and allow clinical applications. We highlighted the multidisciplinary approach of this work by combining engineering abilities and medical experience. Engineering development is essential for the available technological tools and aims to increase neuroscience knowledge in the NFB topic. This technological development was born out of the real clinical need to provide complementary therapeutic solutions to a public health problem, considering the actual clinical context of the post-stroke patient and the practical limits resulting from it.

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“…Sixteen MT-BCI naïve participants were included in this study (aged 22.31 ± 2.33) [23]. EEG signals were recorded using 11 scalp electrodes (FC4, C4, CP4, C6, FC3, C3, CP3, C5, C1, Cz, C2, 10-20 system).…”

Section: ) Data Set 2: Motor Imagery-execution Tasks (Mie) Data Setmentioning

confidence: 99%

Riemannian Channel Selection for BCI With Between-Session Non-Stationarity Reduction Capabilities

Sadatnejad

Lotte

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Between-session non-stationarity is a major challenge of current Brain-Computer Interfaces (BCIs) that affects system performance. In this paper, we investigate the use of channel selection for reducing between-session non-stationarity with Riemannian BCI classifiers. We use the Riemannian geometry framework of covariance matrices due to its robustness and promising performances. Current Riemannian channel selection methods do not consider between-session non-stationarity and are usually tested on a single session. Here, we propose a new channel selection approach that specifically considers non-stationarity effects and is assessed on multi-session BCI data sets. Methods: We remove the least significant channels using a sequential floating backward selection search strategy. Our contributions include: 1) quantifying the non-stationarity effects on brain activity in multi-class problems by different criteria in a Riemannian framework and 2) a method to predict whether BCI performance can improve using channel selection. Results: We evaluate the proposed approaches on three multisession and multi-class mental tasks (MT)-based BCI datasets. They could lead to significant improvements in performance as compared to using all channels for datasets affected by between-session non-stationarity and to significant superiority to the state-of-the-art Riemannian channel selection methods over all datasets, notably when selecting small channel set sizes. Conclusion: Reducing non-stationarity by channel selection could significantly improve Riemannian BCI classification accuracy. Significance: Our proposed channel selection approach contributes to make Riemannian BCI classifiers more robust to between-session non-stationarities.

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Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training

Cited by 17 publications

References 54 publications

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Toward an Adapted Neurofeedback for Post-stroke Motor Rehabilitation: State of the Art and Perspectives

Riemannian Channel Selection for BCI With Between-Session Non-Stationarity Reduction Capabilities

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