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Boxuan Jiang

Publications and source records attributed to Boxuan Jiang.

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More Electrodes, Faster Minds? Rethinking Bandwidth in Brain-Computer Interfaces

High-bandwidth brain--computer interfaces (BCIs) can bypass damaged pathways, reduce motor costs, and improve communication and control. They also inspire visions of accelerated thought output, mind reading, and instant skill acquisition. This Perspective asks how gains in meaningful human I/O scale with interface capacity. We distinguish bandwidth, decodable neural states, neural states, and information a person can use, confirm, and express. Slowly updated task states can unfold into complex behavior through the body, neural control, sensory feedback, the environment, and shared context. Decodable neural activity can support prediction and control; subject-level communication depends on selection, confirmation, and authorization. On the input side, stimulation may guide plasticity and accelerate learning, while embodied skills arise through coordination of a brain, body, and environment. The scaling relationship is likely nonlinear: higher-capacity interfaces can yield real gains, while extreme increases in meaningful human I/O encounter constraints rooted in embodiment, learning, and subject expression.

q-bio.NC

EMGFlow: Robust and Efficient Surface Electromyography Synthesis via Flow Matching

Deep learning-based surface electromyography (sEMG) gesture recognition is frequently bottlenecked by data scarcity and limited subject diversity. While synthetic data generation via Generative Adversarial Networks (GANs) and diffusion models has emerged as a promising augmentation strategy, these approaches often face challenges regarding training stability or inference efficiency. To bridge this gap, we propose EMGFlow, a conditional sEMG generation framework. To the best of our knowledge, this is the first study to investigate the application of Flow Matching (FM) and continuous-time generative modeling in the sEMG domain. To validate EMGFlow across three benchmark sEMG datasets, we employ a unified evaluation protocol integrating feature-based fidelity, distributional geometry, and downstream utility. Extensive evaluations show that EMGFlow outperforms conventional augmentation and GAN baselines, and provides stronger standalone utility than the diffusion baselines considered here under the train-on-synthetic test-on-real (TSTR) protocol. Furthermore, by optimizing generation dynamics through advanced numerical solvers and targeted time sampling, EMGFlow achieves improved quality-efficiency trade-offs. Taken together, these results suggest that Flow Matching is a promising and efficient paradigm for addressing data bottlenecks in myoelectric control systems. Our code is available at: https://github.com/Open-EXG/EMGFlow.

cs.HC