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CISOSE26 Local AI Uedu Code UG26
政治大學 AQI 59 26°C PM2.5 15
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UNDER DEVELOPMENT

Uedu Brain
Brain waves and blood oxygen sensing

Self-built multimodal physiological sensing device integrating EEG, fNIRS and PPG sensing technologies to explore real-time monitoring of cognitive load, attention and learning states.

EEG + fNIRS + PPG — multimodal brain physiology sensing
Uedu Brain · Multimodal probe PROTOTYPE
EEG EEG · band power rel. power 0–100
δ 0.5–4Hz 28
θ 4–8Hz 42
α 8–13Hz 58
β 13–30Hz 74
fNIRS Prefrontal oxygenation Δ µM
HbO₂ +0.42 HbR −0.15 Activation ↑
PPG Pulse oximetry SpO₂ 98%
74bpm
HR
42ms
RMSSD
Prototype schematic · simulated signal, hardware under development

Prototype preview · simulated signals

Three sensing modules

Uedu Brain combines three complementary physiological sensing technologies to capture learners' brain and physiological activity from different perspectives.

EEG

Electroencephalography

Measure brain potential activity via scalp electrodes and capture brain waves in different frequency bands such as α, β, θ and δ, reflecting levels of attention, cognitive load and relaxation.

Alpha wave (8-13 Hz) Beta wave (13-30 Hz) Theta wave (4-8 Hz) Delta wave (0.5-4 Hz)

fNIRS

Functional near-infrared spectroscopy Functional Near-Infrared Spectroscopy

Use near-infrared light to penetrate the scalp and measure haemoglobin oxygenation changes in the prefrontal cortex, indirectly reflecting brain-region activation and cognitive workload.

HbO₂ oxygenated haemoglobin HbR deoxygenated haemoglobin Prefrontal activation

PPG

Photoplethysmography

Detect changes in blood vessel volume through an optical sensor, calculate heart rate and heart rate variability (HRV), and assess autonomic nervous system activity and stress responses.

Heart rate HR HRV (RMSSD) BBI interval SpO₂ blood oxygen

Related research

IEEE BigDataService 2025

An edge-deployed EEG sleep staging collaborative inference framework based on a local LLM

This study explores how to deploy an EEG sleep staging model on edge devices, combining the reasoning capabilities of large language models (LLM) to achieve real-time, privacy-preserving sleep quality assessment. This technology is one of the core algorithms of Uedu Brain.

Read research summary