Artificial Neural Network Codifies Sensory and Cognitive Events Identifying Chaotic Attractors in EEG Signals

Abstract: In past researches our group experimented a method to analyze multiple neural signals by means of a novel self-organizing Artificial Neural Network, highlighting the attractors in which the corresponding dynamic system is evolving. If the attractors show to be chaotic, this means that the neural signals are individually self-organized and, analyzing more signals together, that there is a form of coherence between signals. The ANN can also identify different attractors with a unique code. The ANN allows to attribute the same codes to similar but not identical brain events, reaching the necessary range of flexibility. In the present work the method has been tested on signals from a 14 electrodes EEG system connected to immersive glasses that allow a realistic audiovisual experience. A software procedure synchronizes the acquired signals with various sensory experiences presented in a video. Aim of the research is to characterize sensory and emotional stimuli. The analysis lead to positive results, showing that the binary codes corresponding to similar cognitive and perceptive stimuli are similar, and well differentiated for the codes corresponding to different stimuli.