Introducing our Multimodal Growing Dataset
The engine powering our Foundational and Data Licensing Models
Brainify.AI is building one of the world’s largest and most valuable EEG datasets, with every BREEG device in the field quietly adding to the collection. Each device, along with every supported clinical trial, streams de-identified EEG recordings and rich clinical details straight to our cloud. Over time, this pool of data grows deeper and more nuanced, pairing brain signals with real patient outcomes, diagnoses, and the stories found in clinical notes.
By 2026, as BREEG enters the market, data collection begins in earnest. We expect to cross 15,000 high-quality EEG records by late 2027, a milestone that lets us open the doors to external researchers and pharmaceutical partners. Companies looking for real-world evidence and digital biomarkers can access this resource through annual subscriptions or one-off study licenses, depending on their needs.
The demand for this kind of data is only accelerating. Pharma R&D is shifting fast toward real-world evidence, and neurology-focused digital biomarkers are leading the way. With the global market for real-world evidence expected to nearly triple by 2032, Brainify.AI’s growing dataset is set to become a key ingredient in the next wave of discovery.
A wealth of data value powering our solutions
11 billion
Training samples
150,000
Patients
317,000
Sessions - resting, ERP and sleep
280,000
Clinical notes
103,000
Subjects with treatment
Example of use cases
How many patients exhibit an atypical alpha-to-theta power ratio?
What EEG features are predictive of treatment success in a clinical trial?
What is the average cognitive response latency during complex multi-tasking scenarios?
How do EEG profiles differ between individuals with and without PTSD during emotional stimulus exposure?
Which alpha band signatures are associated with positive outcomes in patients undergoing antidepressant treatment?
What neural dynamics predict the progression of cognitive decline in preclinical Alzheimer's stages?
What percentage of the population demonstrates sleep-stage abnormalities indicative of chronic insomnia?
How do age, gender, and lifestyle factors influence EEG-based biomarkers?
Real-world applications
Understanding sleep architecture, diagnosing sleep disorders, and evaluating the impact of new sleep aids.
Investigating neural activity patterns in conditions like depression, anxiety, schizophrenia, and ADHD.
Detecting seizure onset zones, optimizing treatment protocols, and monitoring patient responses.
Early diagnosis using EEG biomarkers, tracking disease progression, and evaluating interventions.
Especially in early drug development; accelerating the identification of EEG biomarkers that predict drug efficacy or safety.
Studying neural correlates of attention, memory, and decision-making in controlled and real world environments.