Deep learning for c-VEP based brain computer interface systems
| dc.rights.license | CC-BY-NC-ND | |
| dc.contributor.advisor | Krempl, G.M. | |
| dc.contributor.author | Vijayakumar, Rohit | |
| dc.date.accessioned | 2022-08-04T23:00:50Z | |
| dc.date.available | 2022-08-04T23:00:50Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/42135 | |
| dc.description.sponsorship | Utrecht University | |
| dc.language.iso | EN | |
| dc.subject | In this research, the within-subject and leave one subject out (LOSO) performance of deep neural networks (DNNs) on decoding c-VEP responses from EEG data are investigated in terms of accuracy and speed of classification. Improvements in the LOSO performance of the DNN model is probed using transfer learning and dynamic stopping methods. Introspection and visualization of the feature space learned by the model provides an understanding of the spatial and temporal patterns present in c-VEP data. | |
| dc.title | Deep learning for c-VEP based brain computer interface systems | |
| dc.type.content | Master Thesis | |
| dc.rights.accessrights | Open Access | |
| dc.subject.courseuu | Computing Science | |
| dc.thesis.id | 7682 |