Trading time for space: a new approach to investigate the EEG neural mechanisms of fine motor brain based on...

Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limitation makes it challenging to accurately localize and characterize activity within specific target regions of the brain. To address this, we propose a computational model for brain-network analysis based on independent component analysis (ICA) and source-space clustering. First, repetitive ICA decomposition is performed on a trial-by-trial basis, followed by clustering to extract stable independent components and their corresponding spatial mapping vectors. Subsequently, standardized low-resolution brain electromagnetic tomography (sLORETA) is employed for source localization. The resulting source locations are then clustered across trials to define network nodes, which are utilized to construct a source-level brain network for the investigation of neural mechanisms. The efficacy of this algorithm was validated using two datasets: the international Brain-Computer Interface (BCI) competition dataset involving motor imagery, and a self-collected dataset recorded during the preparatory phase of pistol shooting. Analysis of the motor-imagery dataset demonstrated that the proposed method identified active brain regions consistent with those observed in previous functional magnetic resonance imaging (fMRI) studies. Regarding the pistol-shooting preparation dataset, the method revealed heightened activity in the frontal, occipital, and bilateral temporal lobes. Furthermore, the intensity of information interaction among multiple brain regions exhibited a significant correlation with shooting performance. These findings not only corroborate prior research but also uncover novel features regarding source-level functional connectivity. Consequently, this novel framework achieves precise source localization and network analysis using EEG, significantly enhancing spatial resolution and providing a more accurate elucidation of target brain activities and information-interaction mechanisms during motor tasks.

The online version contains supplementary material available at 10.1007/s11571-025-10405-z.