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.

通过使用隐马尔可夫模型和线性混合效应模型从 EEG 数据中提取特征，本研究分析了使用六种机器学习方法对学习者和非学习者进行分类的性能，并比较了不同标准化方法对分类性能的贡献。结果显示，只有使用 theta EEG 数据和深度神经网络时，才能达到超过 80% 的 ROC 曲线下面积，增强了空间学习任务中学习者和非学习者的分类能力。

理解通过脑电数据学习：基于隐马尔可夫模型和混合模型的机器学习与特征工程结合

Understanding learning from EEG data: Combining machine learning and  feature engineering based on hidden Markov models and mixed models

Emotion recognition using electroencephalogram (EEG) mainly has two
scenarios: classification of the discrete labels and regression of the
continuously tagged labels. Although many algorithms were proposed for
classification tasks, there are only a few methods for regression tasks. For
emotion regression, the label is continuous in time. A natural method is to
learn the temporal dynamic patterns. In previous studies, long short-term
memory (LSTM) and temporal convolutional neural networks (TCN) were utilized to
learn the temporal contextual information from feature vectors of EEG. However,
the spatial patterns of EEG were not effectively extracted. To enable the
spatial learning ability of TCN towards better regression and classification
performances, we propose a novel unified model, named MASA-TCN, for EEG emotion
regression and classification tasks. The space-aware temporal layer enables TCN
to additionally learn from spatial relations among EEG electrodes. Besides, a
novel multi-anchor block with attentive fusion is proposed to learn dynamic
temporal dependencies. Experiments on two publicly available datasets show
MASA-TCN achieves higher results than the state-of-the-art methods for both EEG
emotion regression and classification tasks. The code is available at
this https URL

利用电脑辅助情感识别 (EEG) 的主要场景有两种：离散标签的分类和连续标签的回归。虽然有很多算法用于分类任务，但回归任务的方法却很少。对于情感回归，标签在时间上是连续的。一种自然的方法是学习时间动态模式。在以往的研究中，使用长短期记忆 (LSTM) 和时间卷积神经网络 (TCN) 从 EEG 的特征向量中学习时间上下文信息。然而，EEG 的空间模式没有被有效提取。为了使 TCN 能够更好地进行回归和分类任务，并学习空间关系，我们提出了一种名为 MASA-TCN 的新型统一模型。空间感知的时间层使得 TCN 能够在 EEG 电极之间额外学习空间关系。此外，我们还提出了一种新的具有注意力融合的多锚块来学习动态时间依赖关系。在两个公开可用的数据集上的实验结果表明，MASA-TCN 在 EEG 情感回归和分类任务上的结果比最先进的方法更高。代码可在此 https URL 找到。