Proceedings of the ACM International Conference on Computing Frontiers

Decoding EEG and LFP signals using deep learning: heading TrueNorth

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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Authors

Ewan Nurse, Benjamin S. Mashford, Antonio Jimeno Yepes, Isabell Kiral-Kornek, Stefan Harrer, and Dean R. Freestone.

Published on 16 May 2016

Frontiers in Neurology

Access: Open

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 After achieving state of the art classification accuracy of 81% we re-built the neural network into a configuration that is designed to operate within the TrueNorth neuromorphic architecture.

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