Abstract-Several recent studies have reported success in applying EEG-based signal analysis to achieve accurate single-trial classification of responses to visual target detection. Pupil responses are proposed as a complementary modality that can support improved accuracy of single-trial signal analysis. We develop a pupillary response feature-extraction and -selection procedure that helps to improve the classification performance of a system based only on EEG signal analysis. We apply a two-level linear classifier to obtain cognitive-task-related analysis of EEG and pupil responses. The classification results based on the two modalities are then fused at the decision level. Here, the goal is to support increased classification confidence through the inherent modality complementarities. The fusion results show significant improvement over classification performance based on a single modality. Index Terms-Brain-machine interface, decision fusion, EEG, image triage, machine learning, pupillary response, rapid serial visual presentation (RSVP), single-trial detection.
In another work REF , pupil information is used to improve the performance of an image classification system based only on EEG signal analysis.
Decision-Level Fusion of EEG and Pupil Features for Single-Trial Visual Detection Analysis
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering
Medicine Computer Science