Cortical Motor Intention Decoding on an Analog Co- Processor with Fast Training for Non-stationary Data

Cortical Motor Intention Decoding on an Analog Co- Processor with Fast Training for Non-stationary Data
Title:
Cortical Motor Intention Decoding on an Analog Co- Processor with Fast Training for Non-stationary Data
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IEEE Biomedical Circuits and Systems Conference 2017
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Publication Date:
19 October 2017
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Abstract:
This paper presents a low power hardware implementation of a motor intention decoder used in intra-cortical Brain Machine Interfaces. It offers two specific advantages over current state of the art decoders. Firstly, the decoding is done on an analog co-processor instead of a personal computer thereby reducing both the power consumption and size of the overall system. Secondly, the co-processor employs a randomized neural network - extreme learning machine (ELM), which is as quick to train as the linear decoders while being adept at capturing the complex non-linear mappings between the neural activity and the intended movements. Results show an average 10% improvement in decoding accuracy over linear discriminant analysis in non-stationary datasets.
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ISBN:
978-1-5090-5803-7/17
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