A low-power communication scheme for wireless, 1000 channel brain–machine interfaces

Joseph T Costello, Samuel R Nason-Tomaszewski, Hyochan An, Jungho Lee, Matthew J Mender, Hisham Temmar, Dylan M Wallace, Jongyup Lim, Matthew S Willsey, Parag G Patil, Taekwang Jang, Jamie Phillips, Hun-Seok Kim, David Blaauw, Cynthia Chestek

Abstract:

Objective. Brain–machine interfaces (BMIs) have the potential to restore motor function but are
currently limited by electrode count and long-term recording stability. These challenges may be
solved through the use of free-floating ‘motes’ which wirelessly transmit recorded neural signals, if
power consumption can be kept within safe levels when scaling to thousands of motes. Here, we
evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based
motes that aims to reduce the wireless data rate and system power consumption. Approach. To test
PIM’s ability to efficiently communicate neural information, we simulated the communication
scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed
circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and
total power consumption. Main results. We found that PIM at 1 kb/s per channel maintained
strong correlations with true firing rate and matched online BMI performance of a traditional
wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant
performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of
power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels.
Significance. These results suggest that PIM-based communication could significantly reduce
power usage of wireless motes to enable higher channel-counts for high-performance BMIs.