A Non-Linear Stochastic Model for Higher Nervous Activity Based on Experimental Data

In this work we consider phenomena or multisensory convergence as leading to a process of representation which possesses a higher degree of abstraction than it would result from a representation by multiple specific sensory processes considered as a heterogeneous ensemble. It can be immediately noticed some analogy with other systems of the brain, namely with reticular ascending activating system which is formed by networks of cells and their connections, each one responding to multiple sensory heterogeneous efferents. Nevertheless there are some remarkable differences between the two types of multisensory convergence namely the level of representation of associative multisensory systems is more elaborate, more precise and implies a higher level of differentiated abstraction than it is the case with cells of the reticular system witch serve only functions of arousal of the brain or control of the sleep-wakefulness system or attention. We show that the processing of EEG and ERPs signals by the Lee detection method and then by Fourier analysis can be used to identify high abstraction level cognitive processes and emotional states. Finally we present some examples of application of Lee method to ERP and EEG records.