ArticleJournal of healthcare engineering2019
G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery.
Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 39 citations in OpenAlex.
- Correlation between the ERD in grasp/open tasks of BCIs and hand function of stroke patients: a cross-sectional study.Biomedical engineering online · 2023Article
- Functional-oriented, portable brain-computer interface training for hand motor recovery after stroke: a randomized controlled study.Frontiers in neuroscience · 2023Article
- Aberrant beta-band brain connectivity predicts speech motor planning deficits in post-stroke aphasia.Cortex; a journal devoted to the study of the nervous system and behavior · 2022Article
- Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern.Biosensors · 2022Article
- Vibrotactile enhancement in hand rehabilitation has a reinforcing effect on sensorimotor brain activities.Frontiers in neuroscience · 2022Article
- Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface.BioMed research international · 2022Review
- Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Effects of visual-motor illusion on functional connectivity during motor imagery.Experimental brain research · 2021Article
- Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG.Frontiers in human neuroscience · 2021Article
- The Time Course of Perceptual Closure of Incomplete Visual Objects: An Event-Related Potential Study.Computational intelligence and neuroscience · 2020Article
- Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients.Frontiers in neuroscience · 2020Review
- EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface.Journal of healthcare engineering · 2020Article
- Design and Quantitative Analysis of Cancer Detection System Based on Fluorescence Immune Analysis.Journal of healthcare engineering · 2019Article
- Classification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in Electroencephalogram Signal.Basic and clinical neuroscienceArticle
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.
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