Quiles Vicente, Ferrero Laura, Iáñez Eduardo, Ortiz Mario, Gil-Agudo Ángel, Azorín José M
Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain.
Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain.
Front Neurosci. 2023 Mar 14;17:1154480. doi: 10.3389/fnins.2023.1154480. eCollection 2023.
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.
脑机接口（BMI）试图在用户与待控制设备之间建立通信。为了在实际应用领域设计出强大的控制方法，脑机接口面临着巨大挑战。基于脑电图的接口存在伪迹、大量训练数据以及信号的非平稳性等问题，这些都是传统处理技术无法解决的挑战，在实时领域表现出一定的缺点。深度学习技术的最新进展为解决其中一些问题打开了一扇机会之窗。在这项工作中，开发了一种接口，该接口能够检测当人因意外障碍物出现而打算停下时所产生的诱发电位。
首先，该接口在跑步机上对五名受试者进行了测试，当障碍物出现（由激光模拟）时用户停下。分析基于两个连续的卷积网络：第一个用于辨别停下的意图与正常行走，第二个用于纠正前一个网络的误检测。
在交叉验证伪在线分析中，使用两个连续网络的方法比仅使用第一个网络的结果更优。每分钟误报数（FP/min）从31.8降至3.9 FP/min，无误报和真阳性（TP）的重复次数从34.9%提高到60.3%（无FP/TP）。该方法在与外骨骼的闭环实验中进行了测试，其中脑机接口（BMI）检测到障碍物并向其发送停止命令。该方法在三名健康受试者身上进行了测试，在线结果为3.8 FP/min和49.3%（无FP/TP）。为了使该模型在时间框架缩短且可控的情况下适用于身体有残疾的患者，在前述测试中应用并验证了迁移学习技术，然后将其应用于患者。两名不完全性脊髓损伤（iSCI）患者的结果为37.9%（无FP/TP）和7.7 FP/min。
