National Yang Ming Chiao Tung University

NeuroTechnology Laboratory (NTK Lab) /Computational Intelligence in Biomedical Data Science Lab (CIBDS Lab)

You-Yin Chen
https://neurotech.lab.nycu.edu.tw/ ; https://biomedai.lab.nycu.edu.tw

Research Field

Medical Engineering

Introduction

You‑Yin Chen serves a dual role as Principal Investigator of the NeuroTechnology Laboratory (NTK Lab) and the Computational Intelligence in Biomedical Data Science Lab (CIBDS Lab) at National Yang Ming Chiao Tung University. His research leadership uniquely integrates fundamental neurotechnology innovation with regulatory-grade validation and international translation, bridging the full pathway from neural systems research to deployable healthcare solutions.

At NTK Lab, Dr. Chen leads research on neural decoding, neuromodulation, brain-computer interfaces, and advanced neural interfaces, with a strong emphasis on circuit- and network-level mechanisms. His work spans MRI-compatible neural devices, multimodal neuroimaging, and closed-loop neurotechnology platforms for studying and modulating brain networks in neurological and neuropsychiatric disorders. These efforts establish clinically meaningful, pre-translational testbeds that connect neural dynamics with therapeutic and assistive applications.

Complementing this foundational research, Dr. Chen directs CIBDS Lab as an ISO/IEC 17025–accredited independent third-party testing and validation laboratory, providing impartial verification of biomedical devices and medical software. Under his leadership, CIBDS Lab supports functional testing, AI software validation, and regulatory compliance for ECG, PPG, EEG, wearable systems, and hearing devices, including Taiwan’s first and only accredited professional hearing aid testing facility.

By unifying neurotechnology development with regulatory science and international linkage, Dr. Chen enables a seamless translation framework that aligns innovation, clinical validation, and global deployment. This dual-lab structure positions his research program as a strategic bridge between neuroscience, engineering, policy, and healthcare ecosystems worldwide.

1.  NTK Lab Overview (https://neurotech.lab.nycu.edu.tw/)

The NeuroTechnology Laboratory (NTK Lab) develops next-generation neurotechnologies for decoding and modulating brain function across multiple spatial and temporal scales. Integrating neuroscience, biomedical engineering, signal processing, machine learning, and neurophysics, the lab investigates how neural circuits and large-scale brain networks encode information and how disrupted communication and synchronization contribute to neurological and neuropsychiatric disorders.

NTK Lab advances neural recording and neuromodulation platforms, including multichannel electrophysiology, optogenetic and photonic interfaces, DBS-based neuromodulation, and brain-computer interface systems. These technologies are combined with biophysically informed and data-driven decoding frameworks to extract reliable, temporally structured neural representations.

Building on these foundations, the lab extends neural decoding and modulation toward clinical pre-translational neurotechnology. By integrating brain and neuromuscular signals with digital twin modeling and wearable assistive systems, NTK Lab establishes closed-loop platforms for studying sensorimotor adaptation, human-machine interaction, and the durability of neural control, bridging fundamental neuroscience with assistive and therapeutic applications.

2. Computational Intelligence in Biomedical Data Science Lab (CIBDS Lab, https://biomedai.lab.nycu.edu.tw)

The Computational Intelligence in Biomedical Data Science Lab (CIBDS Lab) serves as a strategic hub for the international translation of biomedical AI and medical device technologies. Operating as an ISO/IEC 17025–accredited independent third-party testing laboratory, CIBDS Lab provides impartial, standards-compliant validation of biomedical devices and medical software, supporting global regulatory and market-entry requirements.

The lab offers internationally aligned functional testing and verification services for ECG, PPG, EEG, wearable biomedical devices, and AI-based medical software. Notably, CIBDS Lab established the first and currently the only accredited professional hearing aid testing laboratory in Taiwan, providing electroacoustic evaluations in compliance with IEC 60118 and ANSI S3.22 standards. This capability supports national policy development, clinical quality assurance, and international product certification.

Beyond testing, CIBDS Lab delivers end-to-end translational support, including feasibility assessment, prototype evaluation, clinical study design, regulatory strategy, and post-market performance analysis. This integrated approach enables international partners to reduce regulatory risk and accelerate time-to-market.

CIBDS Lab maintains close collaborations with global industry leaders and national research organizations, including ASUS, Garmin, Microlife, Quanta Computer, as well as Industrial Technology Research Institute, Institute for Information Industry, and Metal Industries Research & Development Centre.

Internationally, the lab actively links Taiwanese innovators with overseas accelerators, clinical validation hubs, and investment networks, enabling effective engagement with U.S. and global healthcare ecosystems. Through regulatory rigor, clinical relevance, and cross-border connectivity, CIBDS Lab strengthens the global deployment of trustworthy biomedical AI technologies.


Research Topics

Track A-Brain Machine Interface Research on Motor Control and Spatial Cognition: This track focuses on brain–machine interface (BMI) research aimed at understanding and decoding neural representations underlying motor control and spatial cognition. Interns will work with neural signals such as electrophysiological recordings (neural spikes and local field potentials) to investigate how the brain encodes movement intention, body dynamics, and spatial information during navigation and action planning.

        Studies in this track emphasize neural decoding, sensorimotor integration, and closed-loop BMI system design, combining experimental paradigms with computational modeling and machine learning–based analysis. Interns may engage in studies involving movement-related neural activity, boundary and spatial representations, or adaptive control frameworks that link brain signals to real-time behavioral or assistive outputs.

        This track provides hands-on experience in BMI research that bridges neural circuit mechanisms with functional motor behavior, supporting applications in neurorehabilitation, assistive technologies, and human–machine interaction.

Track B- Neuromodulation & Brain Network Dynamics: This track investigates how neuromodulation reshapes large-scale brain networks. Projects involve preclinical neuromodulation platforms, stimulation parameter design, and analysis of functional connectivity, network synchronization, and circuit-level plasticity related to cognition and behavior.

Track C-  Multimodal Neuroimaging & Circuit Analysis: Interns in this track analyze multimodal neuroimaging data, including diffusion MRI and functional MRI, to study brain microstructure, connectivity, and circuit organization. Emphasis is placed on linking imaging-derived network metrics with neural function and disease-related alterations.

Track D- AI-Enabled Assistive Neurotechnology & Digital Twins: This track centers on human-centered assistive technologies, integrating neural signals with AI-driven modeling. Interns contribute to digital twin frameworks for rehabilitation, wearable sensing systems, and adaptive control strategies that support personalized neuro-assistive applications.

Track F- EEG Hyperscanning & Social/Cognitive Neuroengineering: This track explores multi-brain recording and analysis during social or interactive tasks. Interns work on EEG hyperscanning experiments, synchronization metrics, and experimental paradigms to study social interaction, joint attention, and collective neural dynamics.


Honor
  • 2026~ Academic Excellence Award, National Yang Ming Chiao Tung University, Taiwan.
    2021-2025 Innovative Improvement Award,  "A Flexible Neural Chip with Multi-Functional Nanostructural Interfaces for Preclinical Application: Diagnosis and Treatment of Brain Diseases", Institute for Biotechnology and Medicine Industry, Taiwan.
  • 2024 Summa Cum Laude and Magna Cum Laude Merit Awards, ISMRM & ISMRT Annual Meeting, Singapore.
    Summa Cum Laude Award: "Prolonged Central Thalamic Intermittent Theta-Burst Stimulation Rescued Memory Deficits in Alzheimer’s Disease Mouse Model";   Magna Cum Laude Merit Award:  "A Novel Deep Learning Denoising Algorithm for Neural Signal Recovery in fMRI".
  • 2023 National Innovation Award, "AI/ML Medical Imaging Standard Platform (MISP)",  Institute for Biotechnology and Medicine Industry, Taiwan.
  • 2023-2025 Academic Excellence Award, National Yang Ming Chiao Tung University, Taiwan.
  • 2023 Summa Cum Laude Award, ISMRM & ISMRT Annual Meeting, Toronto, Canada. "Deep Brain Stimulation of Nucleus Accumbens Alters Brain Functional Connectivity and Metabolism to Enhance Memory-Related Cognitive Function".
  • 2021-2023 Academic Excellence Award, National Yang Ming Chiao Tung University, Taiwan.
    2020 Magna Cum Laude Merit Award, ISMRM–SMRT Virtual Conference (Paris, France). "Altered Brain and Gut in Alzheimer’s Disease Model Using Diffusion MRI and Intestinal Bacteria Gene Analysis". 
  • 2020 Neural Probe Chip Selected as One of the Five Major Innovations, 45th Anniversary of National Yang Ming University, Taiwan.
  • 2018 Magna Cum Laude Merit Awardm Joint Annual Meeting of ISMRM–ESMRMB, Paris, France. "Relationship between Brain and Gut in Autism Spectrum Disorder Using Diffusion MRI and Intestinal Bacteria Gene Analysis". 
  • 2017–2021 Academic Excellence Award, National Yang Ming University, Taiwan.
  • 2016 Summa Cum Laude Award, Joint Annual Meeting and Exhibition of ISMRM & SMRT, Singapore.
  • 2015–2017 Academic Excellence Award, National Yang Ming University, Taiwan.
  • 2014 National Innovation Award, "A Flexible Neural Chip with Multi-Functional Nanostructural Interfaces for Preclinical Application: Diagnosis and Treatment of Brain Diseases",  Institute for Biotechnology and Medicine Industry, Taiwan.
  • 2013 Top 10 Highly Accessed Article, BioMedical Engineering OnLine: “Neurovascular coupling: in vivo optical techniques for functional brain imaging.”
  • 2013 Feature Article, Journal of Neuroscience and Neuroengineering: “A Novel Light-Addressable Multi-Electrode Array Chip for Neural Signal Recording Based on VCSEL Diode Arrays.”
  • 2012 Feature Article, Commentary Highlight, and Candidate Cover Figure, Journal of Cerebral Blood Flow & Metabolism:
    “Transcranial Imaging of Functional Cerebral Hemodynamic Changes in Single Blood Vessels Using In Vivo Photoacoustic Microscopy.” Also highlighted in Research Highlight by Nitish V. Thakor, April 2012.
  • 2009 Research Highlight and Featured Article, Journal of Neuroscience and Nature Reviews Neuroscience:
    “A new scenario for negative functional magnetic resonance imaging signals: endogenous neurotransmission.”

Educational Background

Ph.D. in Department of Electrical Engineering, National Taiwan Univ. 

M.S. in Department of Electrical Engineering, National Taiwan Univ. 

BEng in Dept. of Electrical Engineering, National Chiao Tung Univ. 


Job Description

  1. Perform human pose keypoint annotation on multi-view camera images or videos.
  2. Assist in labeling skeletal joints and tracking human body movements across frames.
  3. Verify and correct annotation results to ensure dataset accuracy and consistency.
  4. Organize and manage labeled datasets for machine learning model training.
  5. Support basic data processing or quality control tasks related to motion capture data.
  6. Collaborate with engineers and researchers to improve annotation workflows.

Preferred Intern Educational Level

Undergraduate or graduate students in fields such as:

  • Computer Science
  • Electrical Engineering
  • Artificial Intelligence
  • Information Engineering
  • Biomedical Engineering
  • Sports Science or related fields

Skill sets or Qualities

  • Basic understanding of computer vision or human pose estimation is a plus.
  • Careful attention to detail and ability to perform accurate data annotation.
  • Familiarity with image/video annotation tools is preferred (not required).
  • Basic knowledge of Python, data processing, or AI datasets is beneficial.
  • Ability to follow annotation guidelines and maintain data consistency.
  • Strong responsibility, patience, and ability to handle repetitive but precise tasks.
  • Interest in motion capture, AI, virtual reality, sports analytics, or human movement analysis.

Job Description

  • Perform annotation and organization of electrophysiological datasets, including neural spike timing and local field potential (LFP) signals.
  • Assist in labeling behavioral events, movement phases, and environmental interaction events during experimental tasks.
  • Support the alignment of neural signals with behavioral and spatial data collected during experiments.
  • Assist in preprocessing neural recordings and conducting quality control of multi-brain-region datasets.
  • Contribute to dataset preparation for studies involving latent space analysis of neural population dynamics.
  • Support research efforts related to brain-inspired neural decoding models that link neural activity to movement or spatial cognition.
  • Document annotation procedures and maintain structured research datasets.

Preferred Intern Educational Level

Undergraduate or graduate students in fields such as:

  • Neuroscience
  • Biomedical Engineering
  • Electrical Engineering
  • Computer Science
  • Cognitive Science
  • Psychology
  • Life Science or related disciplines

Applicants with experience in animal behavioral experiments, neurophysiology labs, or electrophysiological recordings are strongly preferred.

Skill sets or Qualities

  • Basic understanding of neuroscience, neural circuits, or electrophysiological recording techniques.
  • Experience with or familiarity in animal behavioral experiments (e.g., rodent navigation or motor behavior tasks) is highly desirable.
  • Interest in brain machine interfaces, neural decoding, or neural population dynamics.
  • Basic programming skills such as Python or MATLAB for data processing are a plus.
  • Familiarity with concepts such as neural population activity, latent representations, or machine learning for neural data is beneficial.
  • Strong attention to detail when performing data annotation and dataset organization.
  • Ability to work collaboratively in an interdisciplinary research environment.