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Stephen LaConte, PhD
Brain Computer Interfaces Increase Whole-Brain Signal-to-Noise
Assistant Professor, Virginia Tech Carilion Research Institute; Assistant Professor of Biomedical Engineering and Sciences, College of Engineering, Virginia Tech
2 Riverside Circle, Roanoke, VA 24016
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 seminar will present recent studies in which we have found that subject-based control involved frontoparietal 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.