The nonlinear association analysis of the EEG brain data in the process of bistable image perception.
In the present paper the nonlinear association analysis of the EEG brain data in the process of bistable image perception are realized. Brain functional connectivity can be characterized by the temporal evolution of correlation between signals recorded from spatially-distributed regions. Numerous techniques were introduced for assessing this connectivity. Among nonlinear regression analysis methods, we chose a method introduced in the field of EEG analysis by Pijn, Lopes da Silva and colleagues, based on the fitting of a nonlinear curve by piecewise linear approximation, and more recently evaluated in a model of coupled neuronal populations. This method has some major advantages over other signal analysis methods such as coherence and cross-correlation functions because it can be applied independently of whether the type of relationship between the two signals is linear or nonlinear.
In the capacity of bistable image we used a set of images based on a well-known bistable object, the Necker cube, as a visual stimulus. This is a cube with transparent faces and visible ribs; an observer without any perception abnormalities treats the Necker cube as a 3D-object thanks to the specific position of the cube ribs. Bistability in perception consists in the interpretation of this 3D-object as to be oriented in two different ways, in particular, if the different ribs of the Necker cube are drawn with different intensity. In our experimental works we have used the Necker cube images with varying parameter I to be the brightness of the cube wires converging in the right upper inner corner. The brightness of the wires converging in the left lower inner corner is defined as (1 − I).