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A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications

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Abstract

Reliability, scalability and clinical viability are of utmost importance in the design of wireless Brain Machine Interface systems (BMIs). This paper reports on the design and implementation of a neuroprocessor for conditioning raw extracellular neural signals recorded through microelectrode arrays chronically implanted in the brain of awake behaving rats. The neuroprocessor design exploits a sparse representation of the neural signals to combat the limited wireless telemetry bandwidth. We demonstrate a multimodal processing capability (monitoring, compression, and spike sorting) inherent in the neuroprocessor to support a wide range of scenarios in real experimental conditions. A wireless transmission link with rate-dependent compression strategy is shown to preserve information fidelity in the neural data. The optimal design for compression and sorting performance was evaluated for multiple sampling frequencies, wavelet basis choice and power consumption. At 32 channels, the neuroprocessor has been fully implemented on a 5 mm × 5 mm nano-FPGA, and the prototyping resulted in 5.19 mW power consumption, bringing its performance within the power-size constraints for clinical use.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering and Neuroscience Program, Michigan State University, East Lansing, MI, 48824, USA

    Fei Zhang, Mehdi Aghagolzadeh & Karim Oweiss

Authors
  1. Fei Zhang
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  2. Mehdi Aghagolzadeh
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  3. Karim Oweiss
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Correspondence to Karim Oweiss.

Additional information

Manuscript received Jun. 15, 2011. This work was supported by the National Institutes of Health under Grant NS062031.

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Zhang, F., Aghagolzadeh, M. & Oweiss, K. A Fully Implantable, Programmable and Multimodal Neuroprocessor for Wireless, Cortically Controlled Brain-Machine Interface Applications. J Sign Process Syst 69, 351–361 (2012). https://doi.org/10.1007/s11265-012-0670-x

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  • DOI: https://doi.org/10.1007/s11265-012-0670-x

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