Dr. Han  ̾߱



Intelligence to Machine! Freedom to Mankind
From Cognitive Science to Hardware through Information Theory!


Advisor: Soo-Young Lee (CHIPS 203/CMS Bldg. 1001; Tel: 3431/4311, sylee@kaist.ac.kr)




CNSL has been jointly listed at the Department of Electrical Engineering and Brain Science Research Center.


Dual Goals:


- Understanding brain information processing mechanism
- Developing brain-like intelligent systems
  (Artificial Brain and Artificial Cognitive Systems)




 Research Areas:


   The main research areas reside in computational models of brain information processing mechanism and their applications to build human-like intelligent systems, i.e., Artificial Brain and Artificial Cognitive Systems (ACS). These functional models are based on information theory and inspired by findings in cognitive science. Intelligent robots with human-like cognitive functions are examples of ACSs, which improve their functional ability by learning from users and other ACSs.
   Although human-like perception has been regarded as the main achievements on the laboratory, recently the research topics extends further into the higher brain functions including knowledge, emotion, consciousness, and human behavior. In collaboration with American-based NeuroSky Inc., it also works on brain-machine interfaces and neurofeedback mind self-controls both based on EEG and possibly eye-movement.




 Main Achievements:


- auditory models for speech feature extraction, sound localization and blind signal
- top-down selective attention model for robust recognition. (How people see what
  he/she wants to see/hear?)
- multi-modal fusion based on the top-down attention (i.e., audio-visual integration

  for lip-reading)
- feature extraction, selection and adaptation for image, text, emotional speeches,
  music, and EEG.
- neuromorphic chips and boards based on the developed models
- ABrain (Artificial Brain) and OfficeMate (Artificial Secretary) as a testbed of

  human-like intelligent systems




 On-Going Research Projects:


(1) Artificial Cognitive Systems: Proactive Learning and Situation Awareness

     for Robots (2009-2012)



- feature extraction for the recognition of secondary phenomena (such as emotion

  in speeches and EEG signals, facial expressions, and musical timbers, etc.)
- proactive learning algorithm with internal state models for self-identity, emotion,
  and motivation
- knowledge representation and incremental self-learning
- decision-making and socialization models (among people and between human   

  and  robots)
- detection of intentional and un-intentional human desires for intelligent user
  interface (based on EEG, eye-movement, etc.)


(2) Blind Signal Separation and Noise Canceling in Home Environment



- signal separation and enhancement based on Independent Component Analysis
  (ICA) with additional constraints and/or top-down attention (mainly for speech

  and EEG signals)


(3) EEG-based Brain-Machine Interface with NeuroSky



- applications of dry-electrode EEG headset for
  human-oriented user interface for 
game/toys and
  neurofeedback mind controls (in collaboration with

  NeuroSky Inc., USA)


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