从脑电图数据中理解学习:基于隐马尔可夫模型和混合模型的机器学习与特征工程相结合

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Gabriel R. Palma, Conor Thornberry, Seán Commins, Rafael A. Moral
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引用次数: 0

摘要

4-8赫兹的θ振荡在导航任务中的空间学习和记忆功能中发挥着重要作用。额叶θ振荡被认为在空间导航和记忆中发挥着重要作用。脑电图(EEG)数据集非常复杂,因此很难解释与行为相关的神经信号变化。不过,目前有多种分析方法可用于研究复杂的数据结构,特别是基于机器学习的技术。这些方法显示出很高的分类性能,与特征工程的结合增强了它们的能力。本文建议使用隐马尔可夫模型和线性混合效应模型从脑电图数据中提取特征。基于在两次关键试验(第一次和最后一次)和两种条件(学习者和非学习者)下进行空间导航任务时从额叶θ脑电图数据中获得的工程特征,我们分析了六种机器学习方法在对学习者和非学习者参与者进行分类时的性能。我们还分析了用于预处理脑电图数据的不同标准化方法对分类性能的影响。我们将每次试验的分类性能与从相同受试者处收集的数据进行了比较,其中仅包括基于坐标的特征,如空闲时间和平均速度。我们发现,使用基于坐标的数据,更多机器学习方法的分类效果更好。然而,只有深度神经网络在仅使用θ EEG 数据时,其 ROC 曲线下面积高于 80%。我们的研究结果表明,将θ脑电图数据标准化并使用深度神经网络可增强空间学习任务中学习者和非学习者的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structures, especially machine learning-based techniques. These methods have shown high classification performance, and their combination with feature engineering enhances their capability. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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