Learning dynamic regimes of event-based substructures in EEG data using Graph Kernel Koopman Embedding
| dc.contributor.advisor | Mahanti, Prabhat K. | |
| dc.contributor.author | Nagawara Muralinath, Rashmi | |
| dc.date.accessioned | 2025-03-12T12:30:46Z | |
| dc.date.available | 2025-03-12T12:30:46Z | |
| dc.date.issued | 2025-01 | |
| dc.description.abstract | Understanding brain activity requires analyzing EEG data, which is challenging due to the high noise levels, non-linearity, non-stationarity, and individual variability. This thesis introduces a novel methodology using Graph Kernel Koopman Embedding (GKKE) methodology by representing time-evolving brain connectivity as low-dimensional, meta-stable regimes. The study focuses on two critical applications: detecting epileptic seizures (CHB-MIT dataset) and assessing cognitive workload (Cognitive Mental Workload dataset). This research attempts to classify cognitive and neurological states using various combinations of connectivity measures, graph kernels, and classifiers. The results demonstrate that the method has a good classification accuracy of above 85% for both datasets, thus demonstrating its potential to identify intricate patterns. The suggested method involves preprocessing the raw EEG data through which the connectivity matrix is obtained by calculating correlation coefficients and generating gram matrices. Next, we use kernel PCA to simplify the graph features by reducing their dimensions. After that, we test how well they work with machine learning classifiers. | |
| dc.description.copyright | © Rashmi Nagawara Muralinath, 2025 | |
| dc.format.extent | ix, 70 | |
| dc.format.medium | electronic | |
| dc.identifier.uri | https://unbscholar.lib.unb.ca/handle/1882/38264 | |
| dc.language.iso | en | |
| dc.publisher | University of New Brunswick | |
| dc.rights | http://purl.org/coar/access_right/c_abf2 | |
| dc.subject.discipline | Computer Science | |
| dc.title | Learning dynamic regimes of event-based substructures in EEG data using Graph Kernel Koopman Embedding | |
| dc.type | master thesis | |
| oaire.license.condition | other | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of New Brunswick | |
| thesis.degree.level | masters | |
| thesis.degree.name | M.C.S. |