  A macaque monkey is trained to perform two different kinds of tasks, memory
aided and visually aided. In each task, the monkey saccades to eight possible
target locations. A classifier is proposed for direction decoding and task
decoding based on local field potentials (LFP) collected from the prefrontal
cortex. The LFP time-series data is modeled in a nonparametric regression
framework, as a function corrupted by Gaussian noise. It is shown that if the
function belongs to Besov bodies, then using the proposed wavelet shrinkage and
thresholding based classifier is robust and consistent. The classifier is then
applied to the LFP data to achieve high decoding performance. The proposed
classifier is also quite general and can be applied for the classification of
other types of time-series data as well, not necessarily brain data.
