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Decoding EEG and LFP signals using deep learning: heading TrueNorth

Published:16 May 2016Publication History

ABSTRACT

Deep learning technology is uniquely suited to analyse neurophysiological signals such as the electroencephalogram (EEG) and local field potentials (LFP) and promises to outperform traditional machine-learning based classification and feature extraction algorithms. Furthermore, novel cognitive computing platforms such as IBM's recently introduced neuromorphic TrueNorth chip allow for deploying deep learning techniques in an ultra-low power environment with a minimum device footprint. Merging deep learning and TrueNorth technologies for real-time analysis of brain-activity data at the point of sensing will create the next generation of wearables at the intersection of neurobionics and artificial intelligence.

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        • Published in

          cover image ACM Conferences
          CF '16: Proceedings of the ACM International Conference on Computing Frontiers
          May 2016
          487 pages
          ISBN:9781450341288
          DOI:10.1145/2903150
          • General Chairs:
          • Gianluca Palermo,
          • John Feo,
          • Program Chairs:
          • Antonino Tumeo,
          • Hubertus Franke

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          Publication History

          • Published: 16 May 2016

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          CF '16 Paper Acceptance Rate30of94submissions,32%Overall Acceptance Rate240of680submissions,35%

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