Connectivity Analysis of Motor Imagery Paradigm Using Short-Time Features and Kernel Similarities

Abstract

The analysis of coactive regions during a Motor Imagery (MI) task becomes an important issue for revealing the primary neural activity provided by movement intentions. This information should be useful in the design of Brain Computer Interface systems. In this work, a connectivity analysis strategy for the MI paradigm using short-time features and kernel similarities is proposed. Since the imagination and execution of tracking movements are associated with neural rhythm power changes in the μ and β bands, we estimate three representative short-time feature extraction methods (Power spectral density, Hjort, and wavelet parameters). Moreover, a kernel-based pairwise similarity is computed among channels to highlight brain coactive areas during a MI task. In addition, the influence of an EEG preprocessing stage before computing the short-time features and the similarity among channels is studied. The attained results demonstrate that our approach can capture the main brain activity relationships in accordance with the MI paradigm clinic findings.