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.
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
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.