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About

Fengzhen Tang is a researcher at the forefront of neurobiologically inspired robotics and brain-computer interfaces (BCIs). Her work uniquely bridges artificial intelligence, neuroscience, and autonomous systems, with a focus on enabling robots to perceive and navigate the world in ways that mimic biological intelligence. Tang’s most impactful contribution is **NeuroBayesSLAM** (52 citations), a framework that integrates multisensory information for robot navigation using Bayesian principles inspired by the brain, offering a novel approach to simultaneous localization and mapping. She has also made significant strides in **SSVEP-based BCIs**, authoring a comprehensive survey (48 citations) that analyzes deep learning models for direct human-robot communication, and developing the **FB-CCNN** (7 citations), a filter bank complex spectrum convolutional neural network with artificial gradient descent optimization. Her work extends to underwater robotics, where she introduced a scan registration method using symmetrical Kullback–Leibler divergence for mechanical scanning imaging sonar (13 citations), and to quantum-enhanced reinforcement learning for control. Tang’s research is characterized by its interdisciplinary ambition, tackling challenges from visual place recognition in changing environments to brain-inspired perception for micro-biomimetic crawling robots. Her growing citation record reflects her role in shaping the future of intelligent, brain-aware robotic systems.