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Recent research in brain-machine interfaces and devices to treat neurological disease indicate that important network activity exists at temporal and spatial scales beyond the resolution of existing implantable devices. We present innovations in both hardware and software that allow sampling and interpretation of data from brain networks from hundreds or thousands of sensors at submillimeter resolution. These innovations consist of novel flexible, active electrode arrays and unsupervised algorithms for detecting and classifying neurophysiologic biomarkers, specifically high frequency oscillations. We propose these innovations as the foundation for a new generation of closed loop diagnostic and therapeutic medical devices, and brain-machine interfaces.
Viventi, J., Blanco, J., & Litt, B. (2010). Mining Terabytes of Submillimeter-resolution ECoG Datasets for Neurophysiologic Biomarkers. Retrieved from https://repository.upenn.edu/be_papers/167
Date Posted: 18 November 2010
This document has been peer reviewed.