Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/BSN.2011.28 | BSN |
Keywords | Field | DocType |
stringent power consumption constraint,long-term recording,compression ratio,20-db sndr recovery,extra cellular recording,neural action,different neuron,compressive sensing,spike recovery procedure,power consumption,conventional basis pursuit recovery,learned union,cs measurement,discrete wavelet transform,neurophysiology,wireless sensor network,data compression,compressed sensing,action potential,basis pursuit,accuracy,indexing terms,chip,electric potential,wireless sensor networks | Wireless,Computer science,Basis pursuit,Artificial intelligence,Compressed sensing,Recovery procedure,Wavelet,Computer vision,Neurophysiology,Pattern recognition,Compression ratio,Data compression,Machine learning | Conference |
Citations | PageRank | References |
8 | 0.76 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zainul Charbiwala | 1 | 150 | 12.93 |
| Vaibhav Karkare | 2 | 46 | 4.27 |
| Sarah Gibson | 3 | 8 | 1.10 |
| Dejan Markovic | 4 | 811 | 115.54 |
| Mani Srivastava | 5 | 13052 | 1317.38 |