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TrueNorth-enabled real-time classification of EEG data for brain-computer interfacing
- 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.
- Subjects :
- 0301 basic medicine
Computer science
Electroencephalography
Machine learning
computer.software_genre
Convolutional neural network
TrueNorth
Task (project management)
03 medical and health sciences
Epilepsy
0302 clinical medicine
medicine
Humans
Spinal cord injury
Brain–computer interface
Signal processing
Artificial neural network
medicine.diagnostic_test
business.industry
Brain
Signal Processing, Computer-Assisted
Hand
medicine.disease
Pipeline (software)
030104 developmental biology
Brain-Computer Interfaces
Neural Networks, Computer
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Subjects
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