Department of Cognitive & Neural Systems http://hdl.handle.net/2144/1237 2013-05-21T08:59:21Z 2013-05-21T08:59:21Z Guenther, Frank H. Brumberg, Jonathan S. Wright, E. Joseph Nieto-Castanon, Alfonso Tourville, Jason A. Panko, Mikhail Law, Robert Siebert, Steven A. Bartels, Jess L. Andreasen, Dinal S. Ehirim, Princewill Mao, Hui Kennedy, Philip R. http://hdl.handle.net/2144/3349 2012-01-12T07:01:43Z 2009-12-09T00:00:00Z

A Wireless Brain-Machine Interface for Real-Time Speech Synthesis Guenther, Frank H.; Brumberg, Jonathan S.; Wright, E. Joseph; Nieto-Castanon, Alfonso; Tourville, Jason A.; Panko, Mikhail; Law, Robert; Siebert, Steven A.; Bartels, Jess L.; Andreasen, Dinal S.; Ehirim, Princewill; Mao, Hui; Kennedy, Philip R. BACKGROUND. Brain-machine interfaces (BMIs) involving electrodes implanted into the human cerebral cortex have recently been developed in an attempt to restore function to profoundly paralyzed individuals. Current BMIs for restoring communication can provide important capabilities via a typing process, but unfortunately they are only capable of slow communication rates. In the current study we use a novel approach to speech restoration in which we decode continuous auditory parameters for a real-time speech synthesizer from neuronal activity in motor cortex during attempted speech. METHODOLOGY/PRINCIPAL FINDINGS. Neural signals recorded by a Neurotrophic Electrode implanted in a speech-related region of the left precentral gyrus of a human volunteer suffering from locked-in syndrome, characterized by near-total paralysis with spared cognition, were transmitted wirelessly across the scalp and used to drive a speech synthesizer. A Kalman filter-based decoder translated the neural signals generated during attempted speech into continuous parameters for controlling a synthesizer that provided immediate (within 50 ms) auditory feedback of the decoded sound. Accuracy of the volunteer's vowel productions with the synthesizer improved quickly with practice, with a 25% improvement in average hit rate (from 45% to 70%) and 46% decrease in average endpoint error from the first to the last block of a three-vowel task. CONCLUSIONS/SIGNIFICANCE. Our results support the feasibility of neural prostheses that may have the potential to provide near-conversational synthetic speech output for individuals with severely impaired speech motor control. They also provide an initial glimpse into the functional properties of neurons in speech motor cortical areas.

2009-12-09T00:00:00Z Okatan, Murat http://hdl.handle.net/2144/2815 2012-01-10T07:00:56Z 2010-02-03T00:00:00Z

Hippocampal Conceptual Representations and Their Reward Value Okatan, Murat

2010-02-03T00:00:00Z Klein, Arno Mensh, Brett Ghosh, Satrajit Tourville, Jason Hirsch, Joy http://hdl.handle.net/2144/2644 2011-12-30T07:00:32Z 2005-10-05T00:00:00Z

Mindboggle: Automated Brain Labeling with Multiple Atlases Klein, Arno; Mensh, Brett; Ghosh, Satrajit; Tourville, Jason; Hirsch, Joy BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images.

2005-10-05T00:00:00Z Pinna, Baingio Grossberg, Stephen http://hdl.handle.net/2144/2377 2011-11-15T07:04:16Z 2005-03-01T00:00:00Z

Logic and Phenomenology of Incompleteness in Illusory Figures: New Cases and Hypotheses Pinna, Baingio; Grossberg, Stephen Cognitive and gestalt visions theories consider incompleteness to be a necessmy and sufficient factor for inducing illusory figures. The role of incompleteness is studied herein by defining the inner logic subtended by use of the term "incompleteness", presenting new cases to clarify the phenomenology of incompleteness as a necessary and sufficient condition, and suggesting an alternative hypothesis to explain illusory figures after analyzing problems with the incompleteness hypothesis. It is demonstrated that incompleteness is not a sufficient condition, illusory figures do not necessarily complete incompletenesses, the shape of incompleteness does not predict the shape of illusory figures, and incompleteness is not a necessary condition. Finally, it is noted that the incompleteness hypothesis can be replaced by concepts concerning interacting boundary grouping and surface filling-in processes during figure-ground segregation. The suggested hypothesis is consistent with neurophysiological experiments and is described in terms of the FACADE neural model of boundary and surface formation during figure-ground segregation.

2005-03-01T00:00:00Z