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Neurosci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Brain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Material and methods<\/jats:title><jats:p>First, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>The results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP\/min) decreased from 31.8 to 3.9 FP\/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP\/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP\/min and 49.3% NOFP\/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP\/TP and 7.7 FP\/min.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnins.2023.1154480","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T04:52:09Z","timestamp":1678769529000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking"],"prefix":"10.3389","volume":"17","author":[{"given":"Vicente","family":"Quiles","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Ferrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"I\u00e1\u00f1ez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Ortiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1ngel","family":"Gil-Agudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 M.","family":"Azor\u00edn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2917","DOI":"10.1007\/s11063-021-10533-7","article-title":"Deep learning with convnet predicts imagery tasks through eeg","volume":"53","author":"Altan","year":"2019","journal-title":"Neural Process. 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