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.