Brain-Machine Interfaces and Systems Regular Track


Tiago H. Falk (INRS-EMT, Canada)
Jing Jin (East China University of Science and Technology) – pending confirmation
Febo Cincotti, (Sapienza Univ. Rome, Italy) – pending confirmation


Passive and Active Interfaces; Hybrid BMIs

Brain-Machine Interfaces (BMI) were originally conceptualized as an assistive technology allowing locked-in individuals to communicate. Over the years, innovative applications have emerged, including the control of exoskeletons to improve locomotion or spinal cord stimulation neurotechnologies that enable voluntary control of walking in individuals with spinal cord injury. Alternate paradigms have also been perfected to improve communications for non-verbal individuals and new applications in affective computing and human factors have emerged. BMIs can rely on single modalities (e.g., EEG) or on multiple modalities (e.g., EEG-NIRs or EEG-EMG), these latter termed hybrid BMIs. With smaller sensors emerging and their integration into other devices (e.g., virtual reality headsets, headphones, and eyeglasses), with their use for disease diagnostics and for human performance enhancement, BMI applications will only rise. With advances in machine intelligence, the future for BMIs is promising. The Special Track aims at addressing these advances, with interest in the following topics (including, but not limited to):
  • Active BMIs where mental activity is used to control machines
  • Passive/affective BMIs used to measure implicit user information (e.g., fatigue)
  • Hybrid BMIs
  • BMIs and AI/deep learning
  • BMIs for neurorehabilitation
  • BMIs for serious gaming
  • BMI sensors and modalities
  • BMIs for human performance enhancement
  • BMIs and robotics
  • Real-world applications of BMIs
  • Implantable BMIs
  • Neuroethics

Potential TPC Members:

  • Ervin Sejdic, Pitt U, USA                                                        
  • Murat Akcakaya, Pitt U, USA                                                      
  • Francisco Fraga, UFABC, Brazil                                                   
  • Lucas Trambaiolli, Harvard U., USA                                               
  • Tim Mullen, Intheon, USA                                                         
  • Christoph Guger, G.tec, Austria                                                  
  • Ricardo Chavarriaga, EPFL, Switzerland                                           
  • Ferdinand Ephrem, NeurotechX, Canada                                             
  • Yannick Roy, NeurotechX, Canada                                                  
  • Ning Jiang, U. Waterloo, Canada                                                  
  • Teodiano Bastos, UFES, Brazil                                                    
  • Anibal Cointrina, UFES, Brazil                                                   
  • Justin Dauwels, Singapore                                                        
  • Stefanie Blain-Moraes, McGill, Canada                                            
  • Sarah Powers, Memorial University, Canada                                        
  • Hubert Banville, Paris                                                           
  • Thorsten Zander, TU Berlin                                                       
  • Jan-Niklas Antons, TU Berlin                                                     
  • Andrzej Cichocki, Russia                                                         
  • Brandan Allinson, UCSD, USA                                                      
  • Jinling Lian, Beijing U, China