Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking

• Published in: Frontiers in neuroscience Vol. 17; p. 1154480
• Main Authors: Quiles, Vicente, Ferrero, Laura, Iáñez, Eduardo, Ortiz, Mario, Gil-Agudo, Ángel, Azorín, José M
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 14.03.2023; Frontiers Media S.A
• Subjects: Accuracy; Algorithms; BMI; Calibration; closed-loop; Discriminant analysis; EEG; Electrodes; Exoskeleton; Fitness equipment; Interfaces; Neural networks; Neuroscience; Spinal cord injuries; stopping intention; Transfer learning; Germany; exoskeleton; closed-loop; transfer-learning; EEG; BMI; stopping intention
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2023.1154480

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. 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. 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.