Functional disconnection of associative cortical areas predicts performance during BCI training

Corsi, Marie‐Constance; Chávez, Mario; Schwartz, Denis; George, Nathalie; Hugueville, Laurent; Kahn, Ari E.; Dupont, Sophie; Bassett, Danielle S.; Fallani, Fabrizio De Vico

doi:10.1016/j.neuroimage.2019.116500

Cited by 33 publications

(60 citation statements)

References 85 publications

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“…Other predictors are derived from measuring the change in electrophysiological properties across the training sessions [26,27]. Other predictors are derived from measuring the change in electrophysiological properties across the training sessions [26][27][28]. Thus, event-related Des/synchronization (ERD/ERS) is extracted in order to evaluate the (in)efficiency of MI training, which shows a distinct activation of the sensorimotor cortex region in response to imagery tasks [29].…”

Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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Motor Imagery (MI) promotes motor learning in activities, like developing professional motor skills, sports gestures, and patient rehabilitation. However, up to 30% of users may not develop enough coordination skills after training sessions because of inter and intra-subject variability. Here, we develop a data-driven estimator, termed Deep Regression Network (DRN), which jointly extracts and performs the regression analysis in order to assess the efficiency of the individual brain networks in practicing MI tasks. The proposed double-stage estimator initially learns a pool of deep patterns, extracted from the input data, in order to feed a neural regression model, allowing for infering the distinctiveness between subject assemblies having similar variability. The results, which were obtained on real-world MI data, prove that the DRN estimator fosters pre-training neural desynchronization and initial training synchronization to predict the bi-class accuracy response, thus providing a better understanding of the Brain–Computer Interface inefficiency of subjects.

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Section: Introductionmentioning

confidence: 99%

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

et al. 2020

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“…They suggested that higher bilateral theta activity, which leads to lower Mahalanobis distance, may represent greater attention or working memory load and thus results in greater BCI performance. Another study found a negative correlation between BCI scores and relative on-task changes in both high alpha and low beta powers defined by Individual Alpha Frequency (IAF) across four sessions (Corsi et al, 2020). This result is reasonable that the higher decoding BCI scores were associated with a stronger decrease of SMR power during MI, i.e., a larger ERD.…”

Section: Introductionmentioning

confidence: 83%

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Zhou¹,

Lin

Yao

et al. 2021

Front. Hum. Neurosci.

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One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

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“…Functional connectivity (FC), which consists of assessing the interaction between different brain areas [11], can be a valuable tool to provide alternative features to discriminate subjects' mental states [12] and to study neural mechanisms underlying BCI learning [13]. Here, as an exploratory study, we considered complementary undirected FC estimators to assess which of them, associated to Riemannian geometry, could best classify the data.…”

Section: Functional Connectivitymentioning

confidence: 99%

Riemannian Geometry on Connectivity for Clinical BCI

Corsi¹,

Yger²,

Chevallier

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1 st on the task 1 of the competition.

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Functional disconnection of associative cortical areas predicts performance during BCI training

Cited by 33 publications

References 85 publications

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Regression Networks for Neurophysiological Indicator Evaluation in Practicing Motor Imagery Tasks

Relative Power Correlates With the Decoding Performance of Motor Imagery Both Across Time and Subjects

Riemannian Geometry on Connectivity for Clinical BCI

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