Brain Computer Interface (BCI) forges a direct, online communication between brain and machine, independent from the user’s physical abilities and represents a new way to augment human capabilities. They translate the user’s intentions into outputs or actions by means of machine learning techniques. BCI operates either by presenting a stimulus to the operator and waiting for his/her response (synchronous), or continuously monitoring the operator’s cognitive activity and responding accordingly (asynchronous). 

Recent advances in artificial intelligence along with progresses in wearable robotics and sensing systems pave the way towards embedded systems that form a natural extension of human physical abilities. BCI will play a key role in how these systems sense human intentions and integrate their function to become part of our every day life. BCI has already broken down barriers for the physically disabled, restoring the ability to communicate, enhancing rehabilitation for paretic patients and allowing control of movements for paraplegic patients that would otherwise be impossible. These systems advance our understanding of the underlying motor learning mechanisms as they alter the mapping between neuronal activity and feedback control. Furthermore, the technology has been used to assess cognitive states for mental training and attention monitoring in safety critical tasks.

This workshop aims to provide a forum to bring together neuroscientists, engineers, and healthcare practitioners from a diverse range of disciplines to present the current state-of-the-art in Brain Computer Interface research for rehabilitation and health applications. It will also present some of the major technical challenges and unmet healthcare demands that can potentially reshape the future of rehabilitation robotics and wearable devices.

 

 

Confirmed Speakers (in alphabetical order)

  • BCI in Neurorehabilitation
    Ujwal Chaudhary (Niels Birbaumer's group) , University of Tubingen, Germany
  • Neuromuscular Control during Walking in People with Stroke 
    Natalie Mrachacz-Kersting, Aalborg University, Denmark
  • Motor Learning and Neural Interfaces 
    Kianoush Nazarpour, Newcastle University, UK
  • Multi-Class BCI Methods and Adaptive Systems 
    Girijesh Prasad, Ulster University, N. Ireland, UK
  • Visuomotor Adaptation Observed Through Brain-Computer Interface 
    Andrew Schwartz, University of Pittsburgh, PA, USA
  • BCI for Decoding Dexterous Hand and Finger Movements
    Nitish Thakor, John Hopkins University, MD, USA - Presenting via “GoToMeeting”
  • BCI for Robotic Assisted Rehabilitation 
    Yodchanan Wongsawat, 
     Mahidol University, Salaya, Thailand

Topics to be covered

  • Brain-computer interface (BCI)
  • BCI for Assistive technology
  • BCI for Cognitive diagnostics
  • BCI for Robotic rehabilitation
  • Perception and neuro-ergonomics
  • Neuroprostheses and neurorobotics
  • Implants and neuro-interfacing techniques
  • Innovation in imaging and sensing (e,g, fNIRS, EEG, video-oculography)
  • Machine learning and data analytics
  • Decision support based on cognitive load, mental fatigue and hypovigilance detection

Local Organisers and Co-chairs

  • Daniel Leff, Hamlyn Centre, Imperial College London, UK
  • Fani Deligianni, Hamlyn Centre, Imperial College London, UK
  • Guang-Zhong Yang, Hamlyn Centre, Imperial College London, UK

 

Sponsored by:

 

 

EPSRC NIHR-HTC Network Plus