Invasively-obtained signals recorded with electrodes penetrating the brain provide highly detailed information for BMIs. Most of these BMIs have used action potentials, or spikes, from individual neurons. However, with current technologies, spikes can be difficult to record for many years. We have shown that local field potentials (LFPs), which are summed signals from many thousands of neurons, can provide nearly as much information about reaching movements as spikes, even when spikes are not able to be recorded (Flint et al., J. Neural Eng. 2012). We showed for the first time that these LFPs can be used in a BMI to control a cursor with accuracy nearly as good as that of spikes. Further, this performance was stable over a year without having to recalibrate the BMI (Flint et al., J. Neural Eng. 2013). This finding is important because it can enable BMI users to learn a BMI over longer time periods without recalibration. Further, the signals themselves were highly stable over time, particularly in the task-relevant space (Flint et al., J Neurosci 2016).