This project involves measuring workload and emotion while participants view music videos. Uses the DEAP dataset as stimuli and machine learning on cognitive data to develop classification models for new brain states.
This project employed machine learning to see if using ECG/EDA with fNIRS data can successfully categorize human emotion. Result showed 93.39% accuracy. Currently submitted to HCI conference.
This experiment asked: “Can we measure the brain activity of consumers watching advertisements?” The project explores correlating brain activity with user experience while viewing ads to predict product success and market-readiness with a new research modality, fNIRS.
The meditation project involves using fNIRS to detect differences in brain state of individuals meditating. A collaboration with Hamilton State College faculty and students, the meditation experiment is aimed at assessing the effects of meditative states on the brain. Longitudinal studies with vulnerable populations such as trauma victims, veterans, and other local individuals is part of future work.
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