Musuadhi Rajan, Bharath Kumar (2022) Revisiting the Pattern Recognition Methods Employed in the Study of Cognitive Processing Stages. Bachelor's Thesis, Artificial Intelligence.
|
Text
2022_Bharath_Kumar_Musuadhi_Rajan_MSc_Thesis.pdf Download (6MB) | Preview |
|
![[img]](https://fse.studenttheses.ub.rug.nl/style/images/fileicons/text.png) |
Text
toestemming.pdf Restricted to Registered users only Download (117kB) |
Abstract
Cognitive models of ACT-R architecture process information in multiple stages, mimicking the different stages that humans undergo while performing cognitive tasks. For evaluating these models’ stages, cognitive stages underlying the tasks are predicted via multivariate pattern analysis over the features extracted from the brain data of multiple participants and compared with the models’ stages. However, if the techniques used to extract the features do not consider the inter-subject alignment issues and the nonlinear dynamics of brain data, the resulting features might not be a proper representation of the neural activities. To investigate these two issues, 2 feature extraction techniques, MCCA and DMCCA, were applied to the EEG dataset of a recent cognitive study that used PCA for extracting the features from the brain data of 26 participants. Results from the two multivariate pattern analyses, stimuli classification and cognitive stage prediction, showed potential in both techniques for handling inter-subject alignment issues. And the classifier results showed DMCCA’s ability in finding nonlinear patterns. Further investigations, applying MCCA on high-density EEG data and updating the DMCCA architecture, could give better evidence supporting the applicability of the two techniques for inter-subject alignment and nonlinearity respectively.
| Item Type: | Thesis (Bachelor's Thesis) |
|---|---|
| Supervisor name: | Borst, J.P. |
| Degree programme: | Artificial Intelligence |
| Thesis type: | Bachelor's Thesis |
| Language: | English |
| Date Deposited: | 02 Sep 2022 12:44 |
| Last Modified: | 02 Sep 2022 12:44 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/28648 |
Actions (login required)
 |
View Item |