ArticleBioengineering (Basel, Switzerland)2023
Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Polarity-considered EEG microstates improve classification accuracy of oddball stimulus.Frontiers in human neuroscience · 2026Article
- Pre-movement EEG microstates reflect intended lifted load of volitional movement.Frontiers in computational neuroscience · 2026Article
- Quantifying and evaluating motor imagery ability using EEG microstates in MI-BCI training.Experimental brain research · 2025Article
- Acute and Subacute Effects of Session with the EXOPULSE Mollii Suit in a Multiple Sclerosis Patient: A Case Report.Bioengineering (Basel, Switzerland) · 2025Article
- EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.Bioengineering (Basel, Switzerland) · 2025Article
- Data Processing and Machine Learning for Assistive and Rehabilitation Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- Specific endophenotypes in EEG microstates for methamphetamine use disorder.Frontiers in psychiatry · 2024Article
- Effects of acupuncture at limb Acupoints-Guangming (GB37) on UDVA, CS, and EEG microstate in myopia.Frontiers in neuroscience · 2024Article
- Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.Brain sciences · 2023Article
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3 authors.
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Abstract
Motor imagery-based brain-computer interfaces (BCI) have been widely recognized as beneficial tools for rehabilitation applications. Moreover, visually guided motor imagery was introduced to improve the rehabilitation impact. However, the reported results to support these techniques remain unsatisfactory. Electroencephalography (EEG) signals can be represented by a sequence of a limited number of topographies (microstates). To explore the dynamic brain activation patterns, we conducted EEG microstate and microstate-specific functional connectivity analyses on EEG data under motor imagery (MI), motor execution (ME), and guided MI (GMI) conditions. By comparing sixteen microstate parameters, the brain activation patterns induced by GMI show more similarities to ME than MI from a microstate perspective. The mean duration and duration of microstate four are proposed as biomarkers to evaluate motor condition. A support vector machine (SVM) classifier trained with microstate parameters achieved average accuracies of 80.27% and 66.30% for ME versus MI and GMI classification, respectively. Further, functional connectivity patterns showed a strong relationship with microstates. Key node analysis shows clear switching of key node distribution between brain areas among different microstates. The neural mechanism of the switching pattern is discussed. While microstate analysis indicates similar brain dynamics between GMI and ME, graph theory-based microstate-specific functional connectivity analysis implies that visual guidance may reduce the functional integration of the brain network during MI. Thus, we proposed that combined MI and GMI for BCI can improve neurorehabilitation effects. The present findings provide insights for understanding the neural mechanism of microstates, the role of visual guidance in MI tasks, and the experimental basis for developing new BCI-aided rehabilitation systems.
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