Hi, I'mZeyuan Xin
I am a BMI Engineer from Imperial College London, with a focus on intelligent, bidirectional brain-machine interfaces.
About Me
Zeyuan Xin
MEng Molecular Bioengineering student at Imperial College London.
Research Interests:
- Bidirectional Brain–Machine Interface
- Neural Signal Decoding with AI
- Biocompatible Neural Interfaces
- MultiModal Learning
- Computational Neuroscience
- Multi-Embodied Intelligence
Core Technical Focus:
- Neural modulation techniques (e.g., temporal interference, deep brain stimulation)
- Neural signal interpretation and real-time decoding pipelines
- Biocompatible neural interfaces
- Robotic limbs and manipulators as BMI output systems
Long-Term Goal:
To develop intelligent, physically integrated BMI systems that interact with the human body in both form and function, enabling advanced applications in therapy, augmentation, and neurotechnology.
My Skills
Featured Projects
Temporal Interference Simulation
Conducted neural stimulation experiments in NEURON simulation software, comparing TI and tACS effects in rat models.
Tactile Sensor Database Website
Building the Tactile sensor website for four tactile sensors with detailed information and dataset for benchmarking.
Fly Robot Interface (FRI) Neural Control
Bio-inspired robotic control system integrating neural data from fly vision systems for enhanced navigation.
Work & Research Experience
Butterfly Adaptation Analysis
Imperial College London
Motion capture study tracking butterflies under different light conditions using Qualisys Track Manager and MATLAB analysis.
Key Achievements:
- Conducted detailed analysis of butterfly flight trajectories
- Studied wing morphology and adaptation strategies
- Developed mathematical models for flight optimization
- Applied findings to bio-inspired robotics design
Mentors:
Temporal Interference Simulation
Imperial College London
Conducted neural stimulation experiments in NEURON simulation software, comparing TI and tACS effects in rat models using statistical analysis.
Key Achievements:
- Developed comprehensive NEURON simulation models
- Compared temporal interference vs traditional stimulation methods
- Performed statistical analysis on neural response data
- Published findings on stimulation efficacy
Mentors:
Tactile Sensor Database
Imperial College London
Built website for open-access database for vision-based and multimodal tactile sensing in robotics, integrating datasets for MagicTac, GelSight, and ViTacTip sensors.
Key Achievements:
- Developed user-friendly web interface for tactile sensor data
- Integrated multiple sensor datasets into unified platform
- Implemented search and filtering capabilities
- Created comprehensive documentation and user guides
Mentors:
Academic Editorial Intern
MedSciPharmaceutical Technology Co.
Worked as an editorial intern for academic journals, assisting with manuscript review processes and editorial workflows.
Key Achievements:
- Assisted in manuscript review and editorial processes
- Coordinated with authors and reviewers
- Maintained editorial databases and workflows
- Contributed to journal quality improvement initiatives
Mentors:
Fly Robot Interface (FRI) Project
Imperial College London
Improved monocular fly-robot interface by integrating neural data into robotic control, enhancing collision avoidance via binocular system.
Key Achievements:
- Developed advanced neural data integration algorithms
- Implemented binocular vision system for enhanced collision avoidance
- Improved robotic control systems using biological neural patterns
- Successfully completed project with full implementation
Mentors:
VR Lab Training System
Imperial College London
Led development of VR system to reduce bioengineering lab training costs and mitigate safety risks using Unity 3D and C#.
Key Achievements:
- Created immersive VR training environments
- Designed interactive laboratory procedure simulations
- Improved student learning outcomes through VR technology
- Collaborated with educational technology team
Mentors:
CRISPR Project
China Science Academy
Collaborated with research team to assess DNA repair efficiency using CRISPR-Cas9 system in bacteria, achieving 13% improvement in NHEJ.
Key Achievements:
- Designed CRISPR constructs for neural applications
- Analyzed gene editing efficiency in neural cell lines
- Developed protocols for neural tissue modifications
- Contributed to gene therapy research initiatives