利用基于黎曼几何特征的脑电图预测认知负荷。

Iris Kremer, Wissam Halimi, Andy Walshe, Moran Cerf, Pablo Mainar
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引用次数: 0

摘要

目的:我们的研究表明,利用黎曼几何特征进行基于脑电图的认知负荷预测优于现有模型。我们使用黎曼 Procrustes 分析法(RPA)估算了该模型的性能,测试集是在训练过程中未见过的受试者:使用认知负荷分类训练的黎曼均值最小距离模型评估性能。基线性能是使用信号的空间协方差矩阵作为特征确定的。对各种新特征进行了深入探讨和分析,包括根据脑电信号及其一阶导数计算的空间协方差和相关矩阵。此外,还研究了每个 RPA 步骤对性能的影响,并将 RPA 的泛化性能与几种不同的泛化方法进行了比较:通过使用信号一阶导数的空间协方差矩阵作为特征,性能得到了极大改善。此外,这项工作还凸显了 RPA 在认知负荷预测中的重要性和效率:它只需少量校准数据就能实现良好的泛化能力,并在很大程度上优于所有比较方法:使用 RPA 进行认知负荷预测以实现跨科目泛化是一种值得进一步探索的方法,尤其是在校准时间有限的实际应用中。此外,特征探索发现了新的、有前途的特征,这些特征可以在任何黎曼几何设置中使用和进一步实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting cognitive load with EEG using Riemannian geometry-based features.

Objective. We show that electroencephalography (EEG)-based cognitive load (CL) prediction using Riemannian geometry features outperforms existing models. The performance is estimated using Riemannian Procrustes Analysis (RPA) with a test set of subjects unseen during training.Approach. Performance is evaluated by using the Minimum Distance to Riemannian Mean model trained on CL classification. The baseline performance is established using spatial covariance matrices of the signal as features. Various novel features are explored and analyzed in depth, including spatial covariance and correlation matrices computed on the EEG signal and its first-order derivative. Furthermore, each RPA step effect on the performance is investigated, and the generalization performance of RPA is compared against a few different generalization methods.Main results. Performances are greatly improved by using the spatial covariance matrix of the first-order derivative of the signal as features. Furthermore, this work highlights both the importance and efficiency of RPA for CL prediction: it achieves good generalizability with little amounts of calibration data and largely outperforms all the comparison methods.Significance. CL prediction using RPA for generalizability across subjects is an approach worth exploring further, especially for real-world applications where calibration time is limited. Furthermore, the feature exploration uncovers new, promising features that can be used and further experimented within any Riemannian geometry setting.

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