A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials

•Accurate single trial detection of the intention of step initiation from scalp EEG.•Independent component analysis (ICA) preprocessing helps to automatically remove EEG artifacts and enhances detection performance.•All participating subjects were BCI/EEG naïve subjects, implying general applicability of the proposed approach.

ObjectiveApplications of brain–computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detection of gait initiation using movement related cortical potentials (MRCP).MethodsThe templates of MRCP were extracted from 9-channel scalp EEG during gait initiation in 9 healthy subjects. Independent component analysis (ICA) was used to remove artifacts, and the Laplacian spatial filter was applied to enhance the signal-to-noise ratio of MRCP. Following these pre-processing steps, a matched filter was used to perform single-trial detection of gait initiation.ResultsICA preprocessing was shown to significantly improve the detection performance. With ICA preprocessing, across all subjects, the true positive rate (TPR) of the detection was 76.9 ± 8.97%, and the false positive rate was 2.93 ± 1.09 per minute.ConclusionThe results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis.SignificanceThe results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation.