Adaptive decoding using local field potentials in a brain-machine interface.

Brain-machine interface (BMI) systems have the potential to restore function to people who suffer from paralysis due to a spinal cord injury. However, in order to achieve long-term use, BMI systems have to overcome two challenges - signal degeneration over time, and non-stationarity of signals. Effects of loss in spike signals over time can be mitigated by using local field potential (LFP) signals for decoding, and a solution to address the signal non-stationarity is to use adaptive methods for periodic recalibration of the decoding model.