Fully Embedded EEG Intelligence
A wearable EEG system for continuous cognitive state monitoring — fatigue and workload detection running entirely on an ARM Cortex-M4F microcontroller, with no cloud dependency, no wireless streaming, and no external compute. Designed for deployment in environments where connectivity is unavailable or restricted.
The complete EEG pipeline — acquisition, filtering, windowing, spectral decomposition, and classification — runs bare-metal on an ARM Cortex-M4F microcontroller. The architecture requires no operating system, no network interface, and no external processing stage.
Delta, theta, alpha, beta, and gamma band power extracted per epoch via CMSIS-DSP FFT. Feature selection and classifier configuration are evaluated across accuracy, SRAM footprint, and compute cost . Current validation focuses on subject-independent EEG inference under embedded hardware constraints, to better reflect realistic deployment conditions.
Targeted at safety-critical settings where cloud connectivity is unavailable, restricted, or inadmissible — including energy infrastructure, heavy industry, and high-consequence operational roles. On-device inference means the raw EEG signal never leaves the chip.
Most EEG systems — research-grade and commercial — are designed around a wireless streaming assumption. CortiGora's architecture inverts this: the classifier runs on the acquisition hardware itself, and the output is a single inference result. This makes deployment viable in RF-restricted, air-gapped, and connectivity-banned environments.
Operates in RF-restricted, air-gapped, and offline environments. The inference runs entirely on the acquisition hardware — no base station, no paired device, no internet dependency.
The embedded EEG pipeline is being architected for future licensing and OEM integration. The goal is a system that hardware partners can adopt without dependency on CortiGora manufacturing or supply chain — pure embedded software, portable across ARM Cortex-M4F platforms.
The pipeline is developed through peer-reviewed research — feature extraction and classifier selection evaluated across 24 configurations on two EEG datasets, constrained to ARM Cortex-M4F memory and compute limits. The long-term roadmap includes commercialization and intellectual property development.
CortiGora is currently in the research and prototype development stage. We welcome conversations with researchers working in adjacent domains, OEM partners evaluating embedded EEG integration, and operators in environments where cognitive reliability is a functional requirement.
Abdalla Elganbihy
Founder · CortiGora