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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Heelan, C. Komar, J. Vargas-Irwin, C.E. Simeral, J.D. Nurmikko, A.V. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of VA Med. Ctr., Ctr. For Neurorestoration & Neurotechnology, Providence, RI, USA (Simeral, J.D.) || Sch. of Engin., Brown Univ., Providence, RI, USA (Heelan, C.; Komar, J.; Nurmikko, A.V.) || Dept. of Neurosci., Brown Univ., Providence, RI, USA (Vargas-Irwin, C.E.) |
| Abstract | We have designed and implemented a new compact, high performance neural signal processing system for wearable neurotechnology platforms. The low-power embedded system (referred to hereafter as ESPA) prototype wirelessly receives, records, and processes 200 channels of broadband neural data, demonstrated here in a non-human primate. The subject is implanted with intracortical micro-electrode arrays (MEAs) with a head-mounted wireless neural signal transmitting device. The fully integrated embedded system manages and processes neural data using state-of-the art all-programmable system on a Chip (SoC) technology. The utilized SoC provides substantial digital signal processing hardware in the form of a field programmable gate array (FPGA) subsystem. This programmable logic (PL) subsystem connects through high-bandwidth busses to the SoC's mobile processor system (PS) which provides SoC management and communication capabilities through the use of a real-time operating system. The performance of the system was first benchtop tested using 200 channels of 30ksps simulated neural data which underwent neural signal pre-processing, filtering, and feature extraction implemented on the PL subsystem. The raw and processed data were streamed to the PS where it underwent further processing before being communicated wirelessly to a backend server via Wi-Fi. The SoC only required an estimated 2.01W of power during this test. Next, in an application demonstration, a rhesus macaque performed a dexterous manual grasp task. Wirelessly received broadband neural data was communicated by the prototype embedded system to a backend server for real-time file storage and off-line spike feature extraction and hand-grip classification. Successful grip classification was demonstrated by achieving comparable classification accuracy to a conventional rackmounted commercial neural signal processor. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 933989 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781479972340 |
| DOI | 10.1109/BioCAS.2015.7348356 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-22 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | multichannel FPGA mobile ALS Mobile communication paralysis Decoding multielectrode Wireless communication Wireless sensor networks neural prosthetics neural decoding real-time Signal processing embedded system Real-time systems wireless QNX Xilinx medical device Field programmable gate arrays |
| Content Type | Text |
| Resource Type | Article |
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