Principal Investigator: Assist.Prof. Didem GÖKÇAY (CV)

New Program Track for Neuroscience (pdf)



  Schedule of Meetings/Discussions
  Tech Guys
  26.05.2009 Recent Advances in Adaptive Brain Computer Interfaces
  12.05.2009 Learning to Decode Cognitive States from fMRI Data (Gülsen) files
  07.04.2009 Intensity Standardization (Ulaş) files
  31.03.2009 fMRI Adaptation (Didem) files
  24.03.2009 fMRI Adaptation (Didem) files
  10.03.2009 Learned Regulation of Brain Activation (Mete) files
  Funct Guys
  12.05.2009 Looking at Pictures (Arzu-Didem) files
  28.04.2009 fMRI during Video Games (Zeynep) files
  07.04.2009 Cognitive Neuroscience of Emotional Memory (Zeynep)
  03.04.2009 Cognitive Neuroscience of Emotional Memory (Selgün) files
  24.03.2009 Neural Correlates of Processing Valence and Arousal (Hande) files
  Schedule of Experiments
  24.05.2009 fMRI-Face (Ilker)
  05.04.2009 fMRI-Face (Ilker)
  29.03.2009 fMRI-Face (Ilker)
  22.03.2009 fMRI-Face (Ilker)
  15.03.2009 fMRI-Face (Ilker)
  Recent Advances in Adaptive Brain Computer Interfaces
  by: Dr. Fırat İnce
  Time: May 26th, 2009 (Tuesday), 12.40-13.40
  Place: Informatics Institute Technocity building, room Z02 (across from the new sports center, neighboring TUBITAK)
  Recent advances in computational neuroscience show that after appropriate signal processing, the electrical activity of the brain can be used as a new source to help people suffering from spinal cord injury, amyotrophic lateral sclerosis etc.. In this scheme, a brain-computer interface (BCI) records the electrical activity of the brain noninvasively with electroencephalography (EEG) from the surface of the skull, or invasively with electrocorticography (ECoG) from the surface of brain, and processes these activities to be used for communication and control by handicapped people. In this talk I will summarize the recent advances in signal processing and machine learning techniques used for the construction of an adaptive BCI. In particular, the presentation will focus more on the use of multiresolution signal processing and feature extraction algorithms developed by the UMN group for accurate and robust classification of the neural activity. Challenges and future directions will also be covered.