University of Illinois, Dept. of Electrical and Computer Engineering, Urbana-Champaign, IL, USA.
Mayo Clinic, Dept. of Neurology, Rochester, MN, USA.
Sci Rep. 2019 Nov 22;9(1):17390. doi: 10.1038/s41598-019-53925-5.
Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures. Unsupervised clustering of the metrics identified distinct sets of active electrodes across different subjects. In the total population of 11,869 electrodes, our method achieved 97% sensitivity and 92.9% specificity with the most efficient metric. We validated our results with anatomical localization revealing significantly greater distribution of active electrodes in brain regions that support verbal memory processing. We propose our machine-learning framework for objective and efficient classification and interpretation of electrophysiological signals of brain activities supporting memory and cognition.
识别记录与任务相关的神经生理活动的有效电极，无论是在临床和工业应用中，还是在研究大脑功能方面，都是必要的。我们开发了一种无监督的、全自动的方法，用于对 115 名执行自由回忆言语记忆任务的患者的颅内事件相关 EEG（iEEG）响应进行分类。我们的方法采用了新的可解释指标，这些指标基于带内功率和同步性度量来量化归一化 iEEG 信号的频谱特征。基于这些指标的无监督聚类在不同的个体中识别出了不同的有效电极集。在总共有 11869 个电极的情况下，我们的方法在使用最有效的指标时，达到了 97%的灵敏度和 92.9%的特异性。我们通过解剖定位验证了我们的结果，发现与言语记忆处理相关的大脑区域的有效电极分布显著增加。我们提出了我们的机器学习框架，用于客观有效地分类和解释支持记忆和认知的脑活动的电生理信号。
