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Schedule of Meetings/Discussions |
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Tech Guys |
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26.05.2009 |
Recent Advances in Adaptive Brain Computer Interfaces |
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12.05.2009 |
Learning to Decode Cognitive States from fMRI Data (Gülsen) |
files |
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07.04.2009 |
Intensity Standardization (Ulaş) |
files |
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31.03.2009 |
fMRI Adaptation (Didem) |
files |
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24.03.2009 |
fMRI Adaptation (Didem) |
files |
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10.03.2009 |
Learned Regulation of Brain Activation (Mete) |
files |
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Funct Guys |
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12.05.2009 |
Looking at Pictures (Arzu-Didem) |
files |
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28.04.2009 |
fMRI during Video Games (Zeynep) |
files |
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07.04.2009 |
Cognitive Neuroscience of Emotional Memory (Zeynep) |
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03.04.2009 |
Cognitive Neuroscience of Emotional Memory (Selgün) |
files |
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24.03.2009 |
Neural Correlates of Processing Valence and Arousal (Hande) |
files |
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Schedule of Experiments |
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24.05.2009 |
fMRI-Face (Ilker) |
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05.04.2009 |
fMRI-Face (Ilker) |
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29.03.2009 |
fMRI-Face (Ilker) |
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22.03.2009 |
fMRI-Face (Ilker) |
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15.03.2009 |
fMRI-Face (Ilker) |
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Abstracts |
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Recent Advances in Adaptive Brain Computer Interfaces |
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by: Dr. Fırat İnce |
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Time: May 26th, 2009 (Tuesday), 12.40-13.40 |
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Place: Informatics Institute Technocity building, room Z02 (across from the new sports center, neighboring TUBITAK) |
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Abstract: |
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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. |
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