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

Objective: Applications 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).

Methods: The 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.

Results: ICA 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.

Conclusion: The results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis.

Significance: The results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation.