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TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing

Authors :
Stefan Harrer
Dean R. Freestone
David B. Grayden
Dulini Mendis
Ewan S. Nurse
Isabell Kiral-Kornek
Benjamin S. Mashford
Source :
EMBC
Publication Year :
2017
Publisher :
IEEE, 2017.

Abstract

Brain-computer interfaces are commonly proposed to assist individuals with locked-in syndrome to interact with the world around them. In this paper, we present a pipeline to move from recorded brain signals to real-time classification on a low-power platform, such as IBM's TrueNorth Neurosynaptic System. Our results on a EEG-based hand squeeze task show that using a convolutional neural network and a time preserving signal representation strategy provides a good balance between high accuracy and feasibility in a real-time application. This pathway can be adapted to the management of a variety of conditions, including spinal cord injury, epilepsy and Parkinson's disease.

Details

Database :
OpenAIRE
Journal :
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Accession number :
edsair.doi.dedup.....887f22fb57d38a124351e52771474cd7
Full Text :
https://doi.org/10.1109/embc.2017.8037156