title: EEG-Based Detection of Starting and Stopping During Gait Cycle.
abstract: 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.
authors.affiliation: 1 Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Av. de la Universidad s/n, Elche, 03202, Spain.
authors.forename: Enrique
authors.initials: E
authors.lastname: Hortal
authors.affiliation: 1 Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Av. de la Universidad s/n, Elche, 03202, Spain.
authors.forename: Andrés
authors.initials: A
authors.lastname: Úbeda
authors.affiliation: 1 Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Av. de la Universidad s/n, Elche, 03202, Spain.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Iáñez
authors.affiliation: 1 Brain-Machine Interface Systems Lab, Miguel Hernández University of Elche, Av. de la Universidad s/n, Elche, 03202, Spain.
authors.forename: José M
authors.initials: JM
authors.lastname: Azorín
authors.affiliation: 2 Biomedical Neuroengineering Group, Miguel Hernández University of Elche, Av. de la Universidad s/n, Elche, 03202, Spain.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Fernández
mesh_terms: D000293:Adolescent; D000328:Adult; D001696:Biomechanical Phenomena; D001921:Brain / Q000502:physiology* / Q000503:physiopathology; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D000067190:Exoskeleton Device; D005189:False Positive Reactions; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D007866:Leg / Q000502:physiology* / Q000503:physiopathology; D008297:Male; D009128:Muscle Spasticity / Q000503:physiopathology / Q000534:rehabilitation; D012815:Signal Processing, Computer-Assisted; D013119:Spinal Cord Injuries / Q000503:physiopathology / Q000534:rehabilitation*; D000071939:Stroke Rehabilitation / Q000379:methods; D060388:Support Vector Machine; D013997:Time Factors; D055815:Young Adult
keywords: Brain-machine interface; event-related desynchronization; event-related synchronization; gait; rehabilitation