Walking is for humans an essential task in our daily life. However, there is a huge (and growing) number of people who have this ability diminished or are not able to walk due to motor disabilities. In this paper, a system to detect the start and the stop of the gait through electroencephalographic signals has been developed. The system has been designed in order to be applied in the future to control a lower limb exoskeleton to help stroke or spinal cord injured patients during the gait. The brain-machine interface (BMI) training has been optimized through a preliminary analysis using the brain information recorded during the experiments performed by three healthy subjects. Afterward, the system has been verified by other four healthy subjects and three patients in a real-time test. In both preliminary optimization analysis and real-time tests, the results obtained are very similar. The true positive rates are [Formula: see text] and [Formula: see text] respectively. Regarding the false positive per minute, the values are also very similar, decreasing from 2.66 in preliminary tests to 1.90 in real-time. Finally, the average latencies in the detection of the movement intentions are 794 and 798[Formula: see text]ms, preliminary and real-time tests respectively.
Keywords: Brain-machine interface; event-related desynchronization; event-related synchronization; gait; rehabilitation.