ArticleFrontiers in human neuroscience2020
Predicting Motor Imagery Performance From Resting-State EEG Using Dynamic Causal Modeling.
Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Motor imagery-based neurofeedback in older adults: neural signatures and feasibility in a randomized controlled trial targeting age-related cognitive decline.Journal of neuroengineering and rehabilitation · 2026Trial
- iTBS on RDLPFC improves performance of motor imagery: a brain-computer interface study combining EEG and fNIRS.Journal of neuroengineering and rehabilitation · 2025Trial
- Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery.Sensors (Basel, Switzerland) · 2023Trial
- Mirror Therapy Versus Motor Imagery in Stroke Neurorehabilitation: A Systematic Review with Comparative Narrative Synthesis.Life (Basel, Switzerland) · 2026Review
- Exploring the relationship between somatosensory-evoked potentials, resting-state theta power, and acute balance performance.Scientific reports · 2025Article
- Microstructure Abnormalities of Diffusion Tensor Imaging Measures in First-Episode, Treatment-Naïve Adolescents With Major Depressive Disorder: An Integrated AFQ and TBSS Study.Brain and behavior · 2025Article
- Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity.Frontiers in neuroscience · 2025Article
- Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.Frontiers in neuroscience · 2024Article
- Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.Brain sciences · 2023Article
- Neural Activation in the Ventromedial Prefrontal Cortex Precedes Conscious Experience of Being in or out of a Transient Hallucinatory State.Schizophrenia bulletin · 2023Article
- Neuropsychological Evidence Underlying Counterclockwise Bias in Running: Electroencephalography and Functional Magnetic Resonance Imaging Studies of Motor Imagery.Behavioral sciences (Basel, Switzerland) · 2023Article
- Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance.Frontiers in human neuroscience · 2023Review
- Personalized Brain-Computer Interface and Its Applications.Journal of personalized medicine · 2022Review
- Repetitive Peripheral Magnetic Stimulation Combined with Motor Imagery Changes Resting-State EEG Activity: A Randomized Controlled Trial.Brain sciences · 2022Article
- Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface.Entropy (Basel, Switzerland) · 2022Article
- A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Dynamic causal modeling of evoked responses during emergency braking: an ERP study.Cognitive neurodynamics · 2022Article
- Brain functional and effective connectivity based on electroencephalography recordings: A review.Human brain mapping · 2022Review
- A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification.Frontiers in human neuroscience · 2022Article
- Deep Residual Convolutional Neural Networks for Brain-Computer Interface to Visualize Neural Processing of Hand Movements in the Human Brain.Frontiers in computational neuroscience · 2022Article
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3 authors.
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Abstract
Motor imagery-based brain-computer interfaces (MI-BCIs) send commands to a computer using the brain activity registered when a subject imagines-but does not perform-a given movement. However, inconsistent MI-BCI performance occurs in variations of brain signals across subjects and experiments; this is considered to be a significant problem in practical BCI. Moreover, some subjects exhibit a phenomenon referred to as "BCI-inefficiency," in which they are unable to generate brain signals for BCI control. These subjects have significant difficulties in using BCI. The primary goal of this study is to identify the connections of the resting-state network that affect MI performance and predict MI performance using these connections. We used a public database of MI, which includes the results of psychological questionnaires and pre-experimental resting-state taken over two sessions on different days. A dynamic causal model was used to calculate the coupling strengths between brain regions with directionality. Specifically, we investigated the motor network in resting-state, including the dorsolateral prefrontal cortex, which performs motor planning. As a result, we observed a significant difference in the connectivity strength from the supplementary motor area to the right dorsolateral prefrontal cortex between the low- and high-MI performance groups. This coupling, measured in the resting-state, is significantly stronger in the high-MI performance group than the low-MI performance group. The connection strength is positively correlated with MI-BCI performance (Session 1:
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