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In brain-machine interface (BMI) researches, a fast and accurate detection of user intention is an important research issue for an efficient humanmachine interaction. Among electroencephalography (EEG)-based BMI paradigms, a movement-related cortical potential (MRCP) could be useful to recognize user intention due to its characteristics of spontaneity and low latency. Therefore, in our study, we propose a MRCP feature selection method for decoding user intention under the powered exoskeleton environment. We combined two spectral features that were extracted from readiness potential (RP) and movement-monitoring potential (MMP) sections, respectively. In each MRCP section, we estimated optimal frequency bands for the discriminative feature using a nested cross-validation. Five healthy subjects who were wearing the exoskeleton performed a selfinitiated walking task. Our results showed the grand averaged classification accuracy of 87.6%. To our best knowledge, we first validated a single-trial analysis using the MRCP data acquired from self-initiated exoskeleton walking. Our experimental results present that the proposed MRCP-based exoskeleton control system is becoming more feasible for real-world applications.

Decoding of walking Intention under Lower limb exoskeleton Environment using MRCP Feature