Sensing and machine learning to study mental states in people and epilepsy patients
Tests wearable sensors and virtual human conversations to study mental states in adults.
Plain-language title generated from the registry record
Registry title: Novel Multimodal Neural, Physiological, and Behavioral Sensing and Machine Learning for Mental States
Key facts
- Where
- Downey and Los Angeles, United StatesSee the 2 sites
Where in United States
- Rancho Los Amigos National Rehabilitation CenterDowney, United StatesTaking people
- University of Southern CaliforniaLos Angeles, United StatesTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- University of Southern California
- Trial code
- NCT07110688
- Record updated
- 1 October 2025
- Original description
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.Read full original descriptionShow less
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.
For the healthy population, all subjects over the age of 18 who are able and willing to give informed consent will be eligible; For the epilepsy population, inclusion criteria are:; Patients who suffer from drug-resistant epilepsy and already have intracranial EEG (iEEG) electrodes implanted based on clinical criteria for their standard seizure localization (unrelated to our study) will be eligible. Most patients are healthy adults outside of their epilepsy; Subjects >= 18 are only included in this study; All patients with the above conditions and with already-implanted electrodes who are willing to participate and able to cooperate and follow research instructions will be recruited.