Background Brain-computer interfaces (BCIs) based on electroencephalography (EEG) signals have been applied to improve active hand motor function rehabilitation of stroke patients. Besides, the bilateral arm rehabilitation training can effectively promote the recovery of the unilateral affected hand. However, existing studies on BCIs for stroke patients mainly focus on decoding the unilateral affected hand without effectively utilizing the unaffected hand. Objective In this paper, we studied the neural signatures and decoding of bimanual and unimanual motor attempts of hemiplegic stroke patients from EEG signals to explore the neural signature differences between bimanual and unimanual movements of patients and whether the differences can improve movement decoding accuracy. Methods: We designed the experimental paradigm of unimanual and bimanual opening and closing motor attempts. Furthermore, we compared the neural signature differences using movement-related cortical potential (MRCP) and event-related desynchronization (ERD). Results Experimental results from hemiplegic stroke patients indicated that greater MRCP activation was found during bimanual movements than unimanual movements. Although the event-related spectral perturbation (ERSP) varied among patients, it mainly appeared in the [8–30] Hz frequency band, and the ERD activation of bimanual movements was greater than that of unimanual movements in specific frequency bands and brain regions for each patient. The average decoding accuracy of bimanual movements was higher than that of unimanual movements by 4% to 12%. Discussion This work can potentially advance BCI-based active rehabilitation of hand motor function of stroke patients.
Jiarong Wang, Luzheng Bi, Weijie Fei et al.· Frontiers in Human Neuroscie...· 0 citations
Mechanistic simulation and machine learning are powerful but complementary tools: physics-based simulation is interpretable yet computationally expensive and blind to real-world context, whereas machine learning is fast but data-hungry and opaque. Biological systems resolve this tension elegantly, coupling physically grounded mechanoreceptor sensing with higher-level neural interpretation that places those signals in context. Inspired by this layered architecture, we present Mechanics-AI, an open-source framework that mirrors the same sensing-then-interpretation logic computationally. A first learning layer (ML1) emulates expensive finite-element, computational fluid dynamics and multiphysics simulations to produce interpretable physical metrics such as stress, strain, shear, and thermal and moisture fields, while a second layer (ML2) fuses these metrics with heterogeneous real-world metadata to predict categorical outcomes and design recommendations. Eight algorithms are benchmarked automatically, the most accurate is selected for each task, and Shapley additive explanations expose the dominant physical drivers to preserve interpretability. The framework is demonstrated across three independent domains using a single unchanged pipeline: forensic traumatic brain injury prediction, optimisation of a bio-inspired humanoid bioreactor for tissue engineering, and a zero-emission building (ZEBAI) framework that couples thermo-hygro-mechanical simulation with Sobol-sampled surrogate modelling to design sustainable, low-carbon envelopes from recycled aggregate concrete by balancing structural safety, energy and embodied carbon. Despite entirely different physics, data and objectives, the same architecture generalises across all three, showing that bio-inspired, layered coupling of mechanistic simulation and contextual learning offers a reusable, interpretable route to cross-domain engineering prediction and sustainable design.
Yuyang Wei, Weijie Fei, Jiarong Wang et al.· Biomimetics· 0 citations
