Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

   

2009 Research Summary

Signal Acquisition Front-end for Implanted Brain-Machine Interfaces

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Rikky Muller and Jan M. Rabaey

Gigascale Systems Research Center

The realization of a fully implantable brain-machine interface (BMI) chip will enable life-changing opportunities such as the development of motor prosthetics. The signals of interest, action, and local field potentials run at biological timescales in the range of 1-10 kHz and can be as small as 10 µV, which presents the challenge of low signal-to-noise ratio (SNR) at the sensor input. Thus a low-power low-noise signal acquisition front-end is critical to any brain-machine sensor array interface to amplify and convert the neural data for digital signal processing.

A significant amount of work has been devoted to this problem over the last decade, however, the prior art has relied heavily on analog techniques and passives to perform signal conditioning and filtering, which significantly impacts die area and does not result in a scalable solution. We propose an architecture in a fine-line process, which uses feedback from the digital domain to set filter pole locations thereby eliminating the need for the integration of large passive components. Combining over-sampling acquisition and digital signal processing in a reconfigurable system can result in significant implant power and area reduction.

Figure 1
Figure 1: System block diagram

[1]
R. Harrison and C. Charles, "A Low-Power Low-Noise CMOS Amplifier for Neural Recording Applications," IEEE JSSC, Vol. 38, June 2003, pp. 958-965.
[2]
T. Denison, K. Consoer, W. Santa, A. Avestruz, J. Cooley, and A. Kelly, "A 2 µW 100 nV/rtHz Chopper-Stabilized Instrumentation Amplifier for Chronic Measurements of Neural Field Potentials," IEEE JSSC, Vol. 42, No. 12, December 2007.