Brain-Machine Interface System Lab, Miguel Hernández University of Elche, 03202 Elche, Spain.
Instituto de Investigación en Ingeniería de Elche-I3E, Miguel Hernández University of Elche, 03202 Elche, Spain.
Biosensors (Basel). 2022 Jul 22;12(8):555. doi: 10.3390/bios12080555.
In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.
在脑电图文献中，缺乏真实地提出必须实时运行的应用程序接口的异步意图模型。在这项工作中，提出了一种新颖的 BMI 方法来实时检测转弯意图。为此，提出了离线、伪在线和在线分析，以验证 EEG 作为转弯意图的生物标志物。本文提出了一种创建 BMI 的方法，可以区分两类：单调行走和转弯意图。对文献中一些最流行的算法进行了比较。为了过滤信号，使用了两种相关算法：H∞滤波器和 ASR。对于处理和分类，计算了黎曼空间中协方差矩阵的平均值，然后使用不同类型的各种分类器，估计测试样本在黎曼空间中与每个类别的距离。这省去了基于功率的模型和在现实场景中必要的基线校正。在通用选择（适用于任何主题）和个性化选择的交叉验证中，结果平均为 66.2%和 69.6%，最佳滤波器为 H∞。对于伪在线，为每个主题自定义配置的平均 TP 为 40.2%，FP/min 为 9.3；最佳主题获得的 TP 为 43.9%，FP/min 为 2.9。在最终验证测试中，该主题获得了 2.5 FP/min 和 71.43%的准确率，转弯预期为 0.21 秒。
