
University of Osaka develops a high-precision, ultra-energy-efficient EEG system based on waveform similarity and compressed sensing.
Key Details
- 1New EEG measurement system achieves 72μW total power consumption.
- 2The approach leverages waveform similarity and compressed sensing instead of black-box generative AI.
- 3System built using commercially available microcontrollers (nRF52840).
- 4Demonstrated normalized mean squared error (NMSE) of 0.116 across 500 measurements.
- 5Targets wearable, long-term monitoring, and self-powered IoT healthcare devices.
Why It Matters

Source
EurekAlert
Related News

AI Model Improves Prediction of Knee Osteoarthritis Progression Using MRI and Biomarkers
A new AI-assisted model that combines MRI, biochemical, and clinical data improves predictions of worsening knee osteoarthritis.

Photonic Chip Enables Versatile Neural Networks for Imaging and Speech AI
Chinese scientists have developed a reconfigurable integrated photonic chip capable of running diverse neural networks, including those for image and speech processing, with high efficiency.

AI Model Predicts Multiple Genetic Markers from Colorectal Pathology Slides
Researchers developed and validated an AI model that simultaneously detects multiple genetic markers in colorectal cancer tissue slides.