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About

Banghua Yang is a leading researcher at the intersection of brain-computer interfaces (BCIs) and robotic control, with a primary focus on decoding neural signals for assistive technologies. Her work centers on motor imagery (MI) and steady-state visual evoked potentials (SSVEP), developing advanced deep learning and adaptive signal processing methods to enhance the performance of real-world BCI systems. Her most impactful contribution is the M-FANet (Multi-Feature Attention Convolutional Neural Network), a novel architecture for MI decoding that achieves superior extraction of spectral-spatial-temporal features from noisy EEG data, amassing 44 citations since 2024. Yang has also pioneered adaptive algorithms like FBCCA and TRCA for SSVEP-based control, enabling more efficient and responsive brain-controlled robotic arms and hands. Her research extends to real-time EMG-based embedded systems for robotic hand control, demonstrating a commitment to practical, deployable solutions. With a portfolio of papers advancing target detection and system integration, Yang’s work is pivotal in translating BCI research from the lab into functional assistive devices, directly impacting rehabilitation and motor control technologies.