| Autor: |
Kiral-Kornek I, Mendis D, Nurse ES, Mashford BS, Freestone DR, Grayden DB, Harrer S |
| Jazyk: |
angličtina |
| Zdroj: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2017 Jul; Vol. 2017, pp. 1648-1651. |
| DOI: |
10.1109/EMBC.2017.8037156 |
| Abstrakt: |
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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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