The existing deep learning-based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them to brain EEG time series (65-70%). This is because there is a large variation between EEG data of different subjects, so a TSC model cannot generalise on unseen subjects well. In this research project, we investigate self-supervised contrastive learning to encode the EEG data. This way we can better model the distribution of our EEG data before classifying it into different mental statuses. See recent work here . The data that will be used in this project includes (but is not limited to), data captured from Emotiv  brainwear devices.
 Kostas, D., Aroca-Ouellette, S., & Rudzicz, F. (2021). BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data. Frontiers in human neuroscience.