Banville Hubert, Jaoude Maurice Abou, Wood Sean U N, Aimone Chris, Holst Sebastian C, Gramfort Alexandre, Engemann Denis-Alexander
Université Paris-Saclay, Inria, CEA, Palaiseau, France.
InteraXon Inc., Toronto, Canada.
Imaging Neurosci (Camb). 2024 Jun 21;2. doi: 10.1162/imag_a_00189. eCollection 2024.
Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Advances in sensor technology have freed EEG from traditional laboratory settings, making low-cost ambulatory or at-home assessments of brain function possible. While ecologically valid brain assessments are becoming more practical, the impact of their reduced spatial resolution and susceptibility to noise remain to be investigated. This study set out to explore the potential of at-home EEG assessments for biomarker discovery using the brain age framework and four-channel consumer EEG data. We analyzed recordings from more than 5200 human subjects (18-81 years) during meditation and sleep, to predict age at the time of recording. With cross-validated scores between - , prediction performance was within the range of results obtained by recent benchmarks focused on laboratory-grade EEG. While age prediction was successful from both meditation and sleep recordings, the latter led to higher performance. Analysis by sleep stage uncovered that N2-N3 stages contained most of the signal. When combined, EEG features extracted from all sleep stages gave the best performance, suggesting that the entire night of sleep contains valuable age-related information. Furthermore, model comparisons suggested that information was spread out across electrodes and frequencies, supporting the use of multivariate modeling approaches. Thanks to our unique dataset of longitudinal repeat sessions spanning 153 to 529 days from eight subjects, we finally evaluated the variability of EEG-based age predictions, showing that they reflect both trait- and state-like information. Overall, our results demonstrate that state-of-the-art machine-learning approaches based on age prediction can be readily applied to real-world EEG recordings obtained during at-home sleep and meditation practice.
脑电图（EEG）是一种用于量化大规模神经元活动的既定方法，可实现多种实际的生物医学应用，包括脑机接口、癫痫监测和睡眠分期。传感器技术的进步使脑电图摆脱了传统实验室环境的限制，使得对脑功能进行低成本的动态或居家评估成为可能。虽然生态有效度高的脑评估变得越来越实用，但其空间分辨率降低和易受噪声影响的问题仍有待研究。本研究旨在利用脑龄框架和四通道消费级脑电图数据，探索居家脑电图评估在生物标志物发现方面的潜力。我们分析了5200多名18至81岁人类受试者在冥想和睡眠期间的记录，以预测记录时的年龄。交叉验证分数在[具体分数范围]之间，预测性能处于近期专注于实验室级脑电图的基准测试所获得的结果范围内。虽然从冥想和睡眠记录中都成功预测了年龄，但后者的表现更佳。按睡眠阶段分析发现，N2 - N3阶段包含了大部分信号。当将从所有睡眠阶段提取的脑电图特征结合起来时，表现最佳，这表明整个夜间睡眠包含有价值的与年龄相关的信息。此外，模型比较表明信息分布在各个电极和频率上，支持使用多变量建模方法。得益于我们独特的数据集，该数据集包含了8名受试者长达153至529天的纵向重复记录，我们最终评估了基于脑电图的年龄预测的变异性，表明它们反映了特质性和状态性信息。总体而言，我们的结果表明，基于年龄预测的先进机器学习方法可以很容易地应用于在家中睡眠和冥想练习期间获得的实际脑电图记录。
