Bayesian analysis of single trial cortical event-related components
Wilson Truccolo · AIP conference proceedings · 2002
A common technique in neurophysiology is the recording of electric potentials generated by cortical neuronal ensembles in relation to a specific event. The understanding of event-related potentials requires the identification of signals that are relatively phase-locked to a stimulus or event onset (event-related potentials) as well as non-phase locked activities. It is now widely accepted that the recorded phase-locked signal itself is not a homogeneous signal, but instead a combination of different components, which can vary in amplitude and latency from trial to trial. We approach the problem of identifying event-related component waveforms and their trial-to-trial variability from a Bayesian perspective. We employ a signal model consisting of a set of unknown source waveforms each with their own set of trial-to-trial amplitudes and latencies. Differential variability of the sources from trial to trial aids significantly in the identification of the component waveforms. The posterior probability density is derived for a specified number of event-related components using data from single or multiple sensors. The Maximum A Posteriori (MAP) solution is used to obtain the event-related component waveforms and their single trial parameters. The approach is demonstrated using a data set consisting of intracortically recorded local field potentials (LFP) in monkeys performing a visuomotor pattern discrimination task.