Ahmed, Md. SabbirMufdi, Shafat AhnafAshrafee, ZarifLamha, AhmedIslam, Md. Faisal2026-04-192026-04-1920262026-01ID 22301021ID 22301222ID 22301148ID 22301011http://hdl.handle.net/10361/27932Cataloged from PDF version of thesis.Includes bibliographical references (pages 57-61).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2026.While the estimation of cognitive load through eye-tracker data has proven to hold immense potential for Human-Computer Interaction, existing implementations are often hindered by significant inter-subject variability and the need for calibration. Domain adaptation techniques attempt to mitigate these shifts by aligning source & target distributions globally. However, this creates a static model that is unable to account for instance-specific variations, namely physiological differences, lighting changes, sensor dropouts, and changes in hardware setups. Integrating test-time adaptation into the pipeline allows for real-time normalization of the model’s statistics using unlabeled streaming data from the test subject, which has been a challenge in prior studies on creating impartial cognitive load classifiers. This makes the model dynamic, which is validated by benchmarking using the COLET & ADABase datasets, representing varied scenarios, subjects, and setups. Our results show that adding the dimension of test-time adaptation improves generalization accuracy without requiring explicit recalibration, which is crucial as the use of extended reality (XR) headsets and the applications of situationally-aware interfaces are rapidly becoming widespread.61 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Cognitive Load Theory (CLT)Eye-trackingCorrelation alignment (CORAL)Human-Computer interactionExtended Reality (XR)Computer vision.Image processing--Digital techniques.Transfer learning (Machine learning).Human-computer interaction--Mathematical models.Thesis