NDLI: Compressive Sensing of Neural Action Potentials Using a Learned Union of Supports

Content Provider	IEEE Xplore Digital Library
Author	Charbiwala, Z. Karkare, V. Gibson, S. Markovic, D. Srivastava, M.B.
Copyright Year	2011
Abstract	Wireless neural recording systems are subject to stringent power consumption constraints to support long-term recordings and to allow for implantation inside the brain. In this paper, we propose using a combination of on-chip detection of action potentials ("spikes") and compressive sensing (CS) techniques to reduce the power consumption of the neural recording system by reducing the power required for wireless transmission. We empirically verify that spikes are compressible in the wavelet domain and show that spikes from different neurons acquired from the same electrode have subtly different sparsity patterns or supports. We exploit the latter fact to further enhance the sparsity by incorporating a union of these supports learned over time into the spike recovery procedure. We show, using extra cellular recordings from human subjects, that this mechanism improves the SNDR of the recovered spikes over conventional basis pursuit recovery by up to 9.5 dB (6 dB mean) for the same number of CS measurements. Though the compression ratio in our system is contingent on the spike rate at the electrode, for the datasets considered here, the mean ratio achieved for 20-dB SNDR recovery is improved from 26:1 to 43:1 using the learned union of supports.
Starting Page	53
Ending Page	58
File Size	768400
Page Count	6
File Format	PDF
ISBN	9781457704697
DOI	10.1109/BSN.2011.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless sensor networks Electric potential Accuracy Power demand compressed sensing Neurons wireless neural recording systems Discrete wavelet transforms Compressed sensing neural action potentials
Content Type	Text
Resource Type	Article

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