A Novel Neural Recording System Utilising Continuous Time Energy Based Compression
File(s)
Accepted version
Author(s)
Faliagkas, K
Leene, L
Constandinou, TG
Type
Conference Paper
Abstract
This work presents a new data compression method
that uses an energy operator to exploit the correlated energy in
neural recording features in order to achieve adaptive sampling.
This approach enhances conventional data converter topologies
with the power saving of asynchronous systems while maintaining
low complexity & high efficiency. The proposed scheme enables
the transmission of 0:7kS/s, while preserving the features of the
signal with an accuracy of 95%. It is also shown that the operation
of the system is not susceptible to noise, even for signals with 1dB
SNR. The whole system consumes 3:94mWwith an estimated area
of 0:093mm2.
that uses an energy operator to exploit the correlated energy in
neural recording features in order to achieve adaptive sampling.
This approach enhances conventional data converter topologies
with the power saving of asynchronous systems while maintaining
low complexity & high efficiency. The proposed scheme enables
the transmission of 0:7kS/s, while preserving the features of the
signal with an accuracy of 95%. It is also shown that the operation
of the system is not susceptible to noise, even for signals with 1dB
SNR. The whole system consumes 3:94mWwith an estimated area
of 0:093mm2.
Date Issued
2015-05-24
Date Acceptance
2015-01-08
Citation
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015, pp.3000-3003
Publisher
IEEE
Start Page
3000
End Page
3003
Journal / Book Title
2015 IEEE International Symposium on Circuits and Systems (ISCAS)
Copyright Statement
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Source
IEEE International Symposium on Circuits & Systems (ISCAS)
Start Date
2015-05-24
Finish Date
2015-05-27
Coverage Spatial
Lisbon, Portugal