ArticleFrontiers in neuroscience2020
Sensorimotor Functional Connectivity: A Neurophysiological Factor Related to BCI Performance.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Influence of age, cognitive function, attention, and mental state on the effectiveness of EEG-based brain-computer interface device use: a systematic review.Journal of neuroengineering and rehabilitation · 2025Pooled it
- Canonical coherence for the estimation of within- and cross-frequency cortico-kinematic interactions.Scientific reports · 2026Article
- Gaussian Connectivity-Driven EEG Imaging for Deep Learning-Based Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- A Personalized Predictor of Motor Imagery Ability Based on Multi-frequency EEG Features.Neuroscience bulletin · 2025Article
- Altered Muscle-Brain Connectivity During Left and Right Biceps Brachii Isometric Contraction Following Sleep Deprivation: Insights from PLV and PDC.Sensors (Basel, Switzerland) · 2025Article
- Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study.Biomedicines · 2024Article
- Can vibrotactile stimulation and tDCS help inefficient BCI users?Journal of neuroengineering and rehabilitation · 2023Article
- Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety.Neural computing & applications · 2023Article
- Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation.Frontiers in aging neuroscience · 2022Article
- The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.Frontiers in neuroscience · 2022Article
- Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.Sensors (Basel, Switzerland) · 2021Article
- Deep Neural Regression Prediction of Motor Imagery Skills Using EEG Functional Connectivity Indicators.Sensors (Basel, Switzerland) · 2021Article
- Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.Frontiers in human neuroscience · 2021Article
- Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance.Frontiers in neuroscience · 2021Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining "good" and "poor" BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
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