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 structure, especially machine learning based techniques. These methods have shown high classification performance and the combination with feature engineering enhances the capability of these methods. 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 (Polynomial Support Vector Machines, Non-linear Support Vector Machines, Random Forests, K-Nearest Neighbours, Ridge, and Deep Neural Networks) 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.

翻译：4-8赫兹范围内的θ振荡在导航任务中的空间学习和记忆功能中发挥重要作用。前额θ振荡被认为对空间导航和记忆至关重要。脑电图数据集极其复杂，使得与行为相关的神经信号变化难以解释。然而，可用的多种分析方法（尤其是基于机器学习的技术）能够处理复杂数据结构。这些方法已展现出高分类性能，而其特征工程进一步增强了其能力。本文提出采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。基于空间导航任务中两个关键试验（首次与末次）及两种条件（学习者与非学习者）下前额θ脑电图数据所工程化获取的特征，我们分析了六种机器学习方法（多项式支持向量机、非线性支持向量机、随机森林、K近邻、岭回归和深度神经网络）对学习者与非学习者参与者的分类性能。我们还分析了用于预处理脑电图数据的标准化方法对分类性能的贡献，并将各试验的分类性能与从相同受试者获取的数据（包括纯坐标特征，如空闲时间和平均速度）进行比较。研究发现，基于坐标数据时更多机器学习方法表现出更优分类效果；但仅深度神经网络在使用纯θ脑电图数据时实现了超过80%的ROC曲线下面积。我们的结果表明，对θ脑电图数据进行标准化并采用深度神经网络可增强空间学习任务中对学习者与非学习者受试者的分类能力。