Characterization of Kinesthetic Motor Imagery paradigm for wrist and forearm using an algorithm based on the Hurst Exponent and Variogram

Abstract

Kinesthetic Motor Imagery (MKI) has been demonstrated to be a robust paradigm for Brain-Computer Interfaces (BCI). In this paper we present the characterization of KMI paradigm of three tasks of wrist and forearm of the right arm using Hurst exponent and variogram, preceding for ICA to map signals into source space. The results show high persistency an average of 0.76 ± 0.07 for KMI Pronation/Supination (PS), 0.82 ± 0.05 for KMI Flexion-Extension). (FE), and 0.90 ± 0.02 for KMI Abduction-Adduction (AA We found a significant difference between the three KMI tasks, useful for multimodal command in BCI.