WPI - Computer Science Department, MS Thesis Presentation, Shreya Milin Mhalgi, " Towards Mental Workload Time Series Classification and Interpretation for Real-Time Feedback in Brain-Computer Interfacing Video Games.


Shreya Milind Mhalgi

MS Student

WPI – Computer Science


Wednesday, December 14, 2022

Time: 1:30 p.m. – 2:30 p.m.

Location: Fuller Labs 311


Advisor: Prof. Rodica Neamtu

Reader: Prof. Erin Solovey


One of the important factors contributing to the creation of engaging and pleasurable video game experiences is immersion. New Brain-Computer Interfaces (BCIs) enable the computer interfaces to engage with the player’s mind such as detecting the player’s real-time mental status (high/low mental workload) and using it as a real-time input for an immersive game experience.

This research aims to explore sequence similarity in brain signals using our novel brain signal exploration tool BrainEx and improve the classification of cognitive workload level by tuning numerous machine learning algorithms on the mental workload time series (fNIRS) dataset in our customized classification tool NaML. Contributions to this research introduce a dashboard, NeuroHub, which will enable researchers who do not have an extensive Computer Science or Data Science background to conduct data parsing, data mining, and machine learning efficiently on the Functional Near-Infrared Spectroscopy (fNIRS) data.

The results of this research could be applied toward enhancing BCI for interactive, real-time video games by customizing immersive game experiences with real-time feedback.

DEPARTMENT(S): Computer Science