Huihui Zhou

About

Huihui Zhou is a leading researcher at the intersection of cognitive neuroscience and robotics, specializing in brain-computer interfaces (BCIs) and humanoid robot control. Her work focuses on enhancing event-related potentials (ERPs)—specifically N200 and P300 signals—to enable intuitive, telepresence-based robot navigation. Zhou’s major contributions include developing an adaptive ERP model that adjusts command repetition based on an individual’s mental state, significantly improving control in cluttered environments (21 citations). She also pioneered a dual-stimuli approach combined with convolutional neural networks to boost the information transfer rate of ERP-based BCIs, addressing a critical bottleneck in real-time robot command generation (17 citations). Her foundational study on increasing N200 potentials via visual stimuli depicting humanoid robot behavior (24 citations) laid the groundwork for more reliable telepresence systems. Zhou’s research has been cited over 67 times, reflecting its impact on advancing human-robot interaction. Her innovative work on object extraction using P300-based interfaces further demonstrates her commitment to solving real-world challenges in autonomous navigation.

Research Focus

Key Achievements

Top Papers

1. 1
   Increasing N200 Potentials Via Visual Stimulus Depicting Humanoid Robot Behavior

   24 citations · 2015
2. 2
   An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

   21 citations · 2016
3. 3
   A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

   17 citations · 2018
4. 4
   Object Extraction in Cluttered Environments via a P300-Based IFCE

   5 citations · 2017

Key Collaborators

Contact & Links