  We present a method for the real time prediction of punctate events in neural
activity, based on the time-frequency spectrum of the signal, applicable both
to continuous processes like local field potentials (LFP) as well as to spike
trains. We test it on recordings of LFP and spiking activity acquired
previously from the lateral intraparietal area (LIP) of macaque monkeys
performing a memory-saccade task. In contrast to earlier work, where trials
with known start times were classified, our method detects and classifies
trials directly from the data. It provides a means to quantitatively compare
and contrast the content of LFP signals and spike trains: we find that the
detector performance based on the LFP matches the performance based on spike
rates. The method should find application in the development of neural
prosthetics based on the LFP signal. Our approach uses a new feature vector,
which we call the 2D cepstrum.
