Stabilizing the human brain–body regulatory system through ethical, non-invasive neuro-AI. Continuous monitoring of physiological telemetry calculates the Brain Stability Index (BSI) to forecast neural instability before clinical manifestation.
Harnessing bio-kinematic and autonomic telemetry to map neural health. Real-time metrics dynamically streamed to clinical environments.
Human brain function is deeply linked with autonomic regulation. The Central Autonomic Network (CAN) links cortical and subcortical structures—including the prefrontal cortex, insula, amygdala, hypothalamus, and brainstem—to cardiovascular, metabolic, and behavioral responses.
Instability in the CAN manifests as measurable physiological signals. By capturing this bio-telemetry, our models construct a continuous digital twin of neuro-autonomic regulation.
The Brain Stability Index (BSI) is a computational metric derived from multimodal physiological telemetry. It represents the dynamic stability of the neuro-autonomic regulatory system.
Rather than diagnosing a specific disease, the BSI detects patterns of instability that precede clinical events across different domains of brain regulation: cardiovascular, emotional, cognitive, and motor coordination.
Through non-invasive wearable sensors, edge computing, and anomaly detection algorithms, the platform detects deviations from a personalized physiological baseline, alerting clinicians and patients before symptoms manifest.
Interact with the CerebriSensus mobile application dashboard simulator below. Toggling target neurological states alters live telemetry values and adjusts the dynamic EEG wave output.
Select a baseline neural profile to inject into the active telemetry engine:
Our development follows a progressive scientific validation strategy, training machine learning models on clear physiological events and scaling to complex neurodevelopmental domains.
Objective autonomic warning. Detecting cardiovascular collapses using continuous ECG/PPG signals. Provides a clean ground-truth training dataset for core anomaly detection algorithms.
Mood and state regulation. Longitudinal monitoring of sleep cycles, actigraphy, and heart rate variability to identify transitions between manic and depressive states up to seven days in advance.
Cognitive regulation and attention control. Utilizing behavioral telemetry and wearable kinematics to detect intra-individual response variability (IIRV) and motor overflow.
Motor-autonomic stability. Integrating biomechanical sensors and computer vision video analysis to assess gait symmetry, optimize physical rehabilitation, and enable early pediatric screening.
Children with hydrocephalus depend on ventriculoperitoneal shunts. Unfortunately, these systems fail frequently and unpredictably, creating severe anxiety for families and leading to low-yield emergency department visits.
We are developing a home-to-clinic shunt surveillance system integrating non-invasive thermal sensors and pressure-surrogate acoustic signals to triage shunt performance.
A smartphone-based computer vision application designed for infant video analysis. Parents and pediatricians record natural infant movements, which our AI models analyze to detect early motor anomalies correlating with cerebral palsy. This facilitates critical intervention during early neuroplasticity windows.
End-to-end integration with consumer and clinical-grade wearables (sensorized glasses, smart insoles, bands). Real-time telemetry is combined with edge processing algorithms to stream continuous BSI values directly to medical providers via EHR integrations.
Intelligent middleware APIs linking BSI telemetry with neurostimulation hardware (DBS, TMS, VNS). By analyzing neural stability indices in real-time, the platform turns static stimulus systems into adaptive, closed-loop systems, optimizing therapeutic dosage dynamically.
Digital endpoints for CNS-focused pharmaceutical clinical trials. Providing big pharma with continuous, high-fidelity objective measures of mood and motor stability, dramatically reducing patient cohort size requirements and accelerating clinical validation cycles.
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