Neural frame simulation 2

Objective: To develop a method for simulating background EEG based on the premise that the activity from synaptic excitation among populations of neurons can be modeled with the filtered output of a random number generator. Methods: The logarithm of the amplitude of activity was weighted in accordance with 1/f, the log frequency in both temporal (PSDT) and spatial (PSDX) spectra. The activity was spatially smoothed by volume conduction. Further deviation from full randomness was by sustained spatial coherence averaging 25% of total power. The departure from the background state to an active state, as seen in the awake EEG, was simulated by adding segments that were 90% correlated while attenuating by 50% the uncorrelated background activity in those segments. Spatial amplitude modulation was imposed on the correlated noise to create signals that simulated AM patterns. Results: The statistical properties of the EEG were replicated, including the PSDT, PSDX, point spread function (PSF), partitioning of the variance with PCA, and the percentages of correct classification of AM patterns. Conclusions: The essential change that identified a frame in EEG was transient increase in synchrony among a population of cortical neurons in the beta or gamma band of the PSDT. The limitation on classification efficacy was imposed by high variance in AM patterns in successive frames with the same artificial spatial pattern. Significance: This method of simulation provides a test bed with which to develop improved techniques for digital signal processing to extract behaviorally relevant information from the EEG at human-machine interfaces. Neural frame simulation 2 Walter J Freeman