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Project # 56
Machine Learning on EEG for Cognitive Monitoring in AR/MR Experiences
| Team Lead: | Thomas Diaz |
| Other Team Members: | |
| Faculty Advisor(s): | Aera K Leboulluec |
| Department: | IMSE |
| Project Type: | REU |
| Title: | Machine Learning on EEG for Cognitive Monitoring in AR/MR Experiences |
| Abstract: | Advances in Augmented Reality (AR) and Mixed Reality (MR) technologies provide visibility into the cognitive interactions of user experiences, yet understanding users’ cognitive states in real time remains a critical challenge. This research investigates the integration of electroencephalography (EEG) with machine learning techniques to monitor and decode cognitive processes during AR/MR interactions. EEG data was collected from engineering classes at University of Texas at Arlington engaged in immersive AR/MR tasks. Machine learning algorithms are applied to the data to identify neural patterns associated with attention, workload, and engagement and to classify cognitive states and predict task performance. The outcomes of this study are an analysis of students’ cognitive states in real time during highly stressed environments such as taking finals at the end of a semester. Ultimately, this research advances the application of EEG-based cognitive monitoring in immersive environments and contributes to the development of intelligent, user-aware AR/MR technologies. |
| Poster: | View Poster #56 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |