Background

Brain-Computer Interface (BCI) aims to extend the capacity of human brain in directly communicating and interacting with the environment, at assisting, augmenting, or repairing human cognitive or sensory-motor functions.

BCI plays an important role in natural cognition, which is to study the brain and behavior at work. It is also the key research topic in the current two world largest-scale brain research projects:

  • Brain Initiative Project (2014-2024, 1 billion USD) in USA,
  • Human Brain Project (2014-2024, 1.2 billion Euros) in EU.

Research on BCI began in the 1970s at the UCLA, and has become an blooming research area in many world leading institutes nowadays in different countries, including

  • USA (UCSD, Purdue, John Hopkins, Duke, UW-Madison, Georgia Institute of Technology)
  • UK (Oxford, Southampton, U. of Essex, Plymouth University)
  • Switzerland (Swiss Federal Institute of Technology (EPFL), U. of Geneva)
  • Japan (Keio, RIKEN Brain Science Institute)
  • Germany (TU Berlin)
  • Canada (U. of British Columbia, U. Toronto), etc.

CT Lin and his team members have developed novel machine-learning algorithms based on Computational Intelligence (CI) technologies

  • To monitor, maintain, or track the human cognitive states and operating performance,
  • To attack the long-time existing BCI dilemma of user variability, circadian variability, and task variability.

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