Data-Driven Modelling of the Nonlinear Cortical Responses Generated by Continuous Mechanical Perturbations

Cortical responses to external mechanical stimuli recorded by electroencephalography have demonstrated complex nonlinearity with fast dynamics. Hence, the modelling of the human nervous system plays a crucial role in studying the function of the sensorimotor system and can help in disentangling the sensory-motor abnormalities in functional movement disorders. In this paper, a non-parametric model is estimated based on locally-linear neuro-fuzzy structures trained by an evolutive algorithm relying on locally-linear model-tree. In particular, simulation model as well as a multi-step ahead predictor model is considered to describe the nonlinear dynamics governing the cortical response. The proposed modelling method is applied to an experimental dataset representing brain activities from ten young healthy subjects. These electroencephalography signals are recorded while robotic manipulations have been applied to their wrist joints. The obtained results are satisfactory and are also compared to those achieved with different modelling strategies applied to the same benchmark data