Exploring the Role of Visual Guidance in Motor Imagery-Based Brain-Computer Interface: An EEG Microstate-Specific Functional Connectivity Study

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.

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis

“…During slow-wave sleep, the executive control network overlapped the fMRI deactivation of microstate C [ 24 ]. In this paper, the increase in MS3 (microstate C) mean duration in the resting-state EEG before the MI task was another characteristic of the subjects’ better MI-BCI performance, which can be understood as the emergence of executive control networks, and the enhancement of the brain’s autonomic neural processing ability is related to the subjects’ MI-BCI performance [ 34 , 35 ].…”

Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity

“…During cognitive tasks, connectivity between task-related brain regions may strengthen or weaken to support specific cognitive processes [19,20]. Comprehending the influence of brain states on brain functional connectivity holds substantial significance for understanding both brain functionality and behavior, offering critical insights for BCIs and attention cognition [21,22]. Given the complexity of brain networks, this study will use information flow gain to quantitatively outline brain functional connectivity and probe the modulation effects of diverse brain states on brain networks.…”