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Pixel Club: Decoding Neural Patterns for Brain-Computer Interfaces
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Ariel Tankus (Biomedical Engineering, Technion)
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Sunday, 10.4.2011, 11:30
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Room 337-8 Taub Bld.
Brain-computer interfaces (BCI) are devices that can decode physiological signals from the brain and convert them into actions in a manner that reflects the brain’s intention. Their goal is to replace or restore lost function in paralyzed humans by routing movement-relate​d signals from the brain, around damaged parts of the nervous system, to external effectors. My research is aimed at developing a new generation of brain-computer interfaces at the single cell level with human participants. One type of BCIs I currently develop is geared towards direct brain control over object movement, based directly on the goal movement that the brain desires for that object, as distinguished from hand movements that the brain may plan for manipulating the object to achieve this movement. Thus, objects will be moved in the manner the individual desires, but without decoding any hand movement.

The other type of BCI in the focus of my talk is aimed to decode natural speech production from brain activity. Recent data I have collected during speech production and imagery of speech production demonstrate robust differential activation during utterance of different phonemes in single neurons from the entorhinal cortex, anterior cingulate gyrus and superior temporal gyrus. We obtain high accuracy in decoding the phonemes from the neural pattern. Decoding these activations, even with a minimal set of words or phonemes, bears an enormous promise for locked-in patients following, for example, amyotrophic lateral sclerosis (ALS) or stroke, to be able to “speak” again.
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