Experimental Comparison of Hardware-Amenable Spike Detection Algorithms\n for iBMIs

2018/12/11 by Shoeb Shaikh, Rosa Q. So, Shaikh, Shoeb +5
Neuroscience · Engineering · #EEG and Brain-Computer Interfaces #Advanced Memory and Neural Computing #Neural dynamics and brain function

paper · pdf · doi:10.48550/arxiv.1812.04786

Abstract

This paper presents an experiment based comparison of absolute threshold (AT)\nand non-linear energy operator (NEO) spike detection algorithms in\nIntra-cortical Brain Machine Interfaces (iBMIs). Results show an average\nincrease in decoding performance of approx. 5% in monkey A across 28 sessions\nrecorded over 6 days and approx. 2% in monkey B across 35 sessions recorded\nover 8 days when using NEO over AT. To the best of our knowledge, this is the\nfirst ever reported comparison of spike detection algorithms in an iBMI\nexperimental framework involving two monkeys. Based on the improvements\nobserved in an experimental setting backed by previously reported improvements\nin simulation studies, we advocate switching from state of the art spike\ndetection technique - AT to NEO.\n

Citations

• A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System

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