ENI
Embedded Neural Interface Framework
An in-development neural acquisition and processing framework. Channel count and sample rate depend on the acquisition hardware and configuration; performance remains a research target.
Signal Acquisition Modes
ENI research covers four signal types through configurable acquisition pipelines.
ENI Processing Pipeline
Acquisition
Configure acquisition for the attached front end. Channel count and sample rate depend on the acquisition hardware and configuration; limits require measurement on a named setup.
Filtering
Hardware-accelerated bandpass filtering (0.1 Hz – 10 kHz), notch filtering (50/60 Hz), and common-average referencing.
Spike Detection
Threshold-crossing and template-matching spike detection are research pipeline goals; throughput and latency benchmarks are pending for each hardware configuration.
Feature Extraction
Spike waveform features, LFP power bands (delta/theta/alpha/beta/gamma), and coherence measures extracted per channel.
Decoding
EAI-powered neural decoder: population vector decoding, Kalman filter, or deep learning decoder (LSTM/Transformer) for BCI applications.
Output
Decoded neural commands delivered via EIPC to EoS applications, ready for the actuator or downstream service to act on.
Use Cases
Brain-Computer Interface
Research target: evaluate motor-intent decoding for assistive control and publish protocol-specific accuracy results.
Closed-Loop Neurostimulation
Research target: study detect-stimulate-measure feedback loops. No clinical efficacy or treatment claim is made.
Neural Prosthetics
Cochlear implant signal processing, retinal prosthetics, and somatosensory feedback for prosthetic hands.
Research Platforms
High-density Utah array and Michigan probe support. Compatible with Open Ephys, Blackrock, and Intan hardware.