  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.
