EEG single-trial detection of gait speed changes during treadmill walk

Abstract

In this study, we analyse the electroencephalography (EEG) signal associated with gait speed changes (i.e. acceleration or deceleration). For data acquisition, healthy subjects were asked to perform volitional speed changes between 0, 1, and 2 Km/h, during treadmill walk. Simultaneously, the treadmill controller modified the speed of the belt according to the subject's linear speed. A classifier is trained to distinguish between the EEG signal associated with constant speed gait and with gait speed changes, respectively. Results indicate that the classification performance is fair to good for the majority of the subjects, with accuracies always above chance level, in both batch and pseudo-online approaches. Feature visualisation and equivalent dipole localisation suggest that the information used by the classifier is associated with increased activity in parietal areas, where mu and beta rhythms are suppressed during gait speed changes. Specifically, the parietal cortex may be involved in motor planning and visuomotor transformations throughout the online gait adaptation, which is in agreement with previous research. The findings of this study may help to shed light on the cortical involvement in human gait control, and represent a step towards a BMI for applications in post-stroke gait rehabilitation.