Machine learning and electroencephalography for enhanced learning in human-computer interaction

The work investigates the relationships between neurological features, learning, memory, and task performance; making effective predictions of vehicle trajectory based on participant behaviour data; and explaining neural networks to elevate transparency of computer reasoning. Multiple contributions are made in these research areas. Specifically, the P300 is demonstrated as a marker of working memory in virtual training environments, extending understanding of this neural response.

Then, delta and theta band activity are demonstrated as modulating with different aspects of driving performance, allowing improvements in behavioural analysis. Furthermore, integrating neurological data into time series recurrent neural networks is verified as a valid technique for improving vehicle trajectory predictions. Finally, a contribution is made to the AI explanation literature, whereby the genetic algorithm is implemented to explain video classifiers.