CMIV seminar, Håkan Gustafsson
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CMIV-Seminar: Stephen LaConte
| What | CMIV Seminar |
|---|---|
| When |
2012-04-26 from 15:15 to 17:00 |
| Where | Wrannesalen, CMIV |
| Contact Name | Maria Kvist |
| Contact Email | maria.kvist@cmiv.liu.se |
| Contact Phone | 010-103 8610 |
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Titel: "Neurofeedback increases whole-brain signal-to-noise". Bio: Stephen LaConte is a member of the Virginia Tech Carilion Research Institute and an Assistant Professor in the School of Biomedical Engineering and Sciences. He received his BSEE in 1996 from the University of Denver and his PhD in Biomedical Engineering from the University of Minnesota in 2002. He has co-authored over 30 papers in numerous areas of MRI-based neuroimaging. His current research is focused on using supervised learning techniques to develop real-time functional magnetic resonance imaging as a tool for performing adaptive experiments as well as for neurofeedback-based rehabilitation and therapy. Abstract: Overt actions allow us to interact directly with our environment. By definition, though, covert mental activity is unobservable by a third party and does not translate to action in the outside world. Real-time functional magnetic resonance imaging (rtfMRI) is a nascent technology that can convert thought into action by transducing noninvasive brain measurements into a control signal to drive physical devices and computer displays, and enable neurofeedback. We have developed an rtfMRI system that is based on multivariate predictive models (e.g. support vector machines) that determine the relationship between the image data and the corresponding sensory/behavioral conditions (brain states). This talk will present three recent studies, in which we have found that subject-based control involved frontoparietal attention circuitry and increased the signal-to-noise ratio (SNR) of task-related brain activity. Importantly, the enhanced SNR was highly correlated to improved prediction accuracy of brain state classifiers, and because these classifiers serve as the control signal for neurofeedback, this work suggests the exciting possibility that brain-computer interfaces can be substantially enhanced by taking advantage of this effect.
