Detection of lower-limb movement intention from EEG signals
Brain-computer interfaces (BCIs) have been investigated in recent years to transfer the brain activities to external devices as rehabilitation tools in clinical trials. Here we present a BCI to detect lower-limb movement intention from electroencephalography (EEG) signals, combining movement-related cortical potentials (MRCPs) and sensorymotor rhythms (SMRs) with support vector machine (SVM) classification model. We report analysis of the EEG correlates of five healthy subjects while they perform self-paced ankle dorsiflexion. The average detection accuracy was 0.89 ± 0.04, while the latency was − 0.325 ± 0.127 ms with respect to actual movement onset. The combination of these two features has shown significantly better performance (p < 0.01) than the models using either MRCP or SMR. It is also demonstrated that complementary information was employed to boost the detection performance. The proposed paradigm could be further implemented as a brain switch in neurorehabilitation scenarios.