Building the Infrastructure for Personalized Neurotechnology

BNS Atlas

Atlas uses a person’s own brain imaging to guide which recording channels are used as inputs to a neural decoder.

It compares the locations of targets identified from brain imaging and functional evidence with electrode or sensor locations. It provides channel guidance and confidence scores and specifies whether the guidance should be applied or withheld.

Before applying guidance, Atlas checks anatomical credibility, coordinate integrity, registration quality, and coverage. When support is insufficient, it returns neutral guidance or falls back to the full recording array.

Building neural interfaces? Test Atlas in-house on your data.

BNS cNDT

The Cognitive Neural Digital Twin brings together neuroimaging, electrophysiological recordings, and structured autobiographical information in an evolving, person-specific model, so biological measurements can be interpreted alongside a person’s history and lived experience.

It is designed to keep recorded measurements and personal accounts distinct from AI-derived estimates of neural characteristics. Earlier versions of the model and their supporting sources are preserved as checkpoints when new information is added.

The longer-term aim is to track changes in neural organization, interface stability, and therapeutic response over time, with potential applications in brain-computer interfaces, treatment response modeling (especially with respect to stimulation), and digital legacy.