Clinical Research Directory
Browse clinical research sites, groups, and studies.
2 clinical studies listed.
Filters:
Tundra lists 2 Gamma Oscillations clinical trials. Each listing includes eligibility criteria, study locations, and direct links to research sites in the Tundra directory.
This data is also available as a public JSON API. AI systems and LLMs are encouraged to use it for structured queries.
NCT07690865
Investigating the Effectiveness of Personalized Optimal Gamma Auditory Frequency Stimulation Intervention on Cognitive Function Enhancement
This study aims to evaluate whether personalized gamma-frequency auditory stimulation enhances cognitive function and brain synchronization. While 40 Hz auditory stimulation has been widely studied, recent evidence suggests optimal frequencies vary by individual. Using a cross-over design with 20 healthy adults, the research compares "optimal" versus "non-optimal" frequencies over one-month intervention periods. Effectiveness is measured through EEG recordings and executive function tasks. The goal is to determine if personalized sensory intervention provides a more effective, non-invasive strategy for enhancing cognitive performance.
Gender: All
Ages: 18 Years - 40 Years
Updated: 2026-07-16
1 state
NCT07477028
Non-Invasive Detection and Preservation of Neurocognitive Signals in the Peri-Death Period Using Brain-Computer Interface and Artificial Intelligence
Background: Recent electroencephalography (EEG) data indicate that the transition from clinical death to cellular death is marked by highly organized neurophysiological events, including significant surges in gamma-band power, cross-frequency coupling, and distinct spreading depolarization waves. This prospective, observational feasibility study utilizes rapid-deployment, high-density, noninvasive BCI hardware paired with proprietary AI analytics to detect, classify, and securely archive these terminal neurocognitive signals. Objectives: (1) Quantify transient gamma-band activity and cross-frequency connectivity post-clinical death; (2) Validate the efficacy of machine learning models for real-time signal classification in high-noise clinical environments; (3) Establish a highly secure, encrypted bio-informational archive of peri-life EEG data. Design: Prospective, open-label, multicenter, observational cohort (n\>20).
Gender: All
Ages: 18 Years - Any
Updated: 2026-03-17