- Title
- A 128-channel 6 mW wireless neural recording IC with spike feature extraction and UWB transmitter
- Creator
- Chae, Moo Sung; Yang, Zhi; Yuce, Mehmet R.; Hoang, Linh; Liu, Wentai
- Relation
- IEEE Transactions on Neural Systems and Rehabilitation Engineering Vol. 17, Issue 4, p. 312-321
- Publisher Link
- http://dx.doi.org/10.1109/TNSRE.2009.2021607
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- journal article
- Date
- 2009
- Description
- This paper reports a 128-channel neural recording integrated circuit (IC) with on-the-fly spike feature extraction and wireless telemetry. The chip consists of eight 16-channel front-end recording blocks, spike detection and feature extraction digital signal processor (DSP), ultra wideband (UWB) transmitter, and on-chip bias generators. Each recording channel has amplifiers with programmable gain and bandwidth to accommodate different types of biological signals. An analog-to-digital converter (ADC) shared by 16 amplifiers through time-multiplexing results in a balanced trade-off between the power consumption and chip area. A nonlinear energy operator (NEO) based spike detector is implemented for identifying spikes, which are further processed by a digital frequency-shaping filter. The computationally efficient spike detection and feature extraction algorithms attribute to an auspicious DSP implementation on-chip. UWB telemetry is designed to wirelessly transfer raw data from 128 recording channels at a data rate of 90 Mbit/s. The chip is realized in 0.35 μm complementary metal-oxide-semiconductor (CMOS) process with an area of 8.8 times 7.2 mm² and consumes 6 mW by employing a sequential turn-on architecture that selectively powers off idle analog circuit blocks. The chip has been tested for electrical specifications and verified in an ex vivo biological environment.
- Subject
- Digital signal processing (DSP); integrated circuit (IC); low-noise amplifier; neural recording system; ultra-wideband (UWB)
- Identifier
- uon:7750
- Identifier
- http://hdl.handle.net/1959.13/808736
- Identifier
- ISSN:1534-4320
- Rights
- Copyright © 2009 IEEE. Reprinted from the IEEE Transactions on Neural Systems and Rehabilitation Engineering Vol. 17, Issue 4, p. 312-321. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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