Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals
Abstract
As human–machine teams are being considered for a variety of mixed‐initiative tasks, detecting and being responsive to human cognitive states, in particular systematic cognitive states, is among the most critical capabilities for artificial systems to ensure smooth interactions with humans and high overall team performance. Various human physiological parameters, such as heart rate, respiration rate, blood pressure, and skin conductance, as well as brain activity inferred from functional near‐infrared spectroscopy or electroencephalogram, have been linked to different systemic cognitive states, such as workload, distraction, or mind–wandering among others. Whether these multimodal signals are indeed sufficient to isolate such cognitive states across individuals performing tasks or whether additional contextual information (e.g., about the task state or the task environment) is required for making appropriate inferences remains an important open problem.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 30, 2023
- Source ID
- 10.1111/tops.12669
Entities
People
- Ayca Aygun
- Boyang Lyu
- Cristianne Fernandez
- J.p. De Ruiter
- Matthias J Scheutz
- Sergio Fantini
- Shuchin Aeron
- Thuan Nguyen
- Zachary Haga
Organizations
- Air Force Office of Scientific Research
- Tufts University