Coding mental states from EEG signals and evaluating their integrated information content : a computational intelligence approach

The paper presents a method to identify and code mental states from EEG signals, performing their dynamical analysis by means of an Artificial Neural Network. 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 the sensory experiences presented in a video. A suitable Artificial Neural Network detects and codifies the chaotic attractors signals related to the sensory and cognitive events. The analysis shows that the binary codes corresponding to similar cognitive and perceptive stimuli are similar, and well differentiated from the codes corresponding to different stimuli. The dynamical attractors corresponding to each mental state are submitted to a procedure that evaluates their Integrated Information content in the qualia space.