Novel Multimodal Neural, Physiological, and Behavioral Sensing and Machine Learning for Mental States

In this program, the investigators will develop novel multimodal neural-behavioral-physiological monitoring tools (software and hardware), and machine learning models for mental states within social processes and beyond. The tools consist of a multimodal skin-like wearable sensor for physiological and biochemical sensing; a conversational virtual human platform to evoke naturalistic social processes; audiovisual affect recognition software; synchronization tools; and machine learning methods to model the multimodal data. The investigators will demonstrate the tools in healthy subjects without neural recordings and in patients with drug-resistant epilepsy who already have intracranial EEG (iEEG) electrodes implanted based on clinical criteria for standard monitoring to localize seizures, which is unrelated to our study.

NCT Number	NCT07110688
Lead Sponsor	University of Southern California Collaborators: National Institute of Mental Health (NIMH)
Conditions	No Condition, Study Mental State in Healthy Populations and Drug-resistant Epilepsy Patients With Existing iEEG
Enrollment	90 participants
Start Date	2024-08-01
Primary Completion	2028-11-30 (estimated)
Study Completion	2028-11-30 (estimated)
Updated on ClinicalTrials.gov	2025-10-01