Neural correlates of learning in a linear discriminant analysis brain-computer interface paradigm.

With practice, the control of brain-computer interfaces (BCI) would improve over time; the neural correlate for such learning had not been well studied. We demonstrated here that monkeys controlling a motor BCI using a linear discriminant analysis (LDA) decoder could learn to make the firing patterns of the recorded neurons more distinct over a short period of time for different output classes to improve task performance.Using an LDA decoder, we studied two Macaque monkeys implanted with microelectrode arrays as they controlled the movement of a mobile robotic platform.