Figure 3.

BRAND can be used for low-latency iBCI control. (a) To test end-to-end iBCI control latency, we ran a graph that received 30 kHz 96-channel neural spiking data via UDP (Ethernet) from two Blackrock NSPs (total of 192 channels), extracted spiking features at 1 kHz, binned spikes into 10 millisecond bins, ran decoding, and updated the location of the cursor in the task. This test used a recurrent neural network (RNN) decoder. This graph was benchmarked using simulated data. (b) Latency measurements for each node were plotted as histograms (N = 30 000 packets). (c) The cumulative latency is plotted relative to the time at which each node (vertical axis) wrote its output to the Redis database. On the horizontal axis, zero is the time at which the last sample in each bin was received over the network from the NSPs. (d) Cursor positions during iBCI-enabled cursor control.