Sitting and Standing Intention Detection based on the Complexity of EEG Signal

摘要

Based on the brain signals, decoding the gait features to make a reliable prediction of action intention is the core issue in the brain computer interface (BCI) based hybrid rehabilitation and intelligent walking aid robot system. In order to realize the classification and recognition of the most basic gait processes such as standing, sitting and quiet, this paper proposes a feature representation method based on the signal complexity and entropy of signal in each brain region. Through the statistical analysis of the parameters between different conditions, these characteristics which sensitive to different actions are determined as a feature vector, and the classification and recognition of these actions are completed by combing support vector machine, linear discriminant analysis and logistic regression. Experimental result shows that the proposed method can better realize the recognition of the above-mentioned action intention. The recognition accuracy of standing, sitting and quiet of 13 subjects is higher than 81%, and the highest one can reach 87%. The result has significant value for understanding human’s cognitive characteristics in the process of lower limb movement and carrying out the study of BCI based strategy and system for lower limb rehabilitation.