ArticleFrontiers in human neuroscience2021
Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces.
Article in Frontiers in human neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- PG-MCTFormer: A Prior-Guided Multi-Scale Convolutional Transformer for Interpretable Motor Imagery EEG Classification.Biomimetics (Basel, Switzerland) · 2026Article
- EEG coherence signatures of sport-specific motor imagery in elite freestyle skiing aerials athletes.Frontiers in neuroscience · 2026Article
- FRFNet: a fatigue-robust EEG-EMG fusion network for hand movement intention recognition.Frontiers in human neuroscience · 2026Article
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
- Takens-Based Kernel Transfer Entropy Connectivity Network for Motor Imagery Classification.Sensors (Basel, Switzerland) · 2025Article
- Improved filter bank common spatial pattern algorithm based on the sparrow search algorithm.Frontiers in human neuroscience · 2025Article
- Research on mental fatigue during long-term motor imagery: a pilot study.Scientific reports · 2024Article
- Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.Brain computer interfaces (Abingdon, England) · 2024Article
- Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography.Frontiers in neuroscience · 2024Article
- Can vibrotactile stimulation and tDCS help inefficient BCI users?Journal of neuroengineering and rehabilitation · 2023Article
- Social robots as effective language tutors for children: empirical evidence from neuroscience.Frontiers in neurorobotics · 2023Article
- Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.Frontiers in neuroscience · 2023Article
- Bridging the BCI illiteracy gap: a subject-to-subject semantic style transfer for EEG-based motor imagery classification.Frontiers in human neuroscience · 2023Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.Frontiers in neuroscience · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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