Learning that adapts to attention
MonmonAI can use optional attention signals as learning context while keeping the learner in control.
EEG learning signals
Connect attention signals to learning loops.
Use an optional EEG learning session to support focus-aware study and review.
EEG product vision
Attention signals become learning context.
These visuals show how MonmonAI can connect optional EEG-style signals with dashboards, review timing, and focus-aware learning support.
Intelligent review with EEG insight
Focus and signal quality can help review sessions understand when a word may need calmer reinforcement.
Personalized learning dashboard
Session summaries keep EEG-style signals connected to learning progress, not medical claims.
Eye tracking and EEG learning insights
Gaze, attention, and ordinary quiz outcomes can work together as optional support signals.
How it works
Simple rules for using EEG safely in learning.
MonmonAI treats EEG as optional study context. The app looks for stable, usable signals and keeps ordinary learning results at the center of every decision.
EEG learning signals give MonmonAI extra context about focus, fatigue, and signal quality during study. They help the app understand the learning moment; they do not replace quizzes, recall history, or your own judgment.
Baseline is your personal reference. MonmonAI first records a quiet rest period, then a normal reading period, so later signals can be compared with your own usual state instead of a generic score.
Repeat baseline if the headset moves, contact quality is weak, the room changes, or the reading feels unusual. The newest saved baseline becomes the reference for that session.
Blinks, jaw movement, poor headset contact, and sudden motion can make a sample unreliable. MonmonAI flags weak samples before using them for learning support.
Voice Effort Review works without EEG by listening for silence, hesitation, pitch, volume, retries, and incomplete spoken attempts. EEG can add separate focus or fatigue context when the learner enables it.
EEG learning signals are optional. You choose whether to start a session, and the EEG privacy controls are kept separate from ordinary vocabulary learning.
This feature is learning support only. It is not diagnosis, treatment, clinical neurofeedback, or a medical attention measurement.
Muse 2 roadmap
Connect your Muse 2 to MonmonAI and start a session.
Follow these steps in order. Install BlueMuse, then keep BlueMuse and the MonmonAI connector running while the learning session is active.
Prepare Muse 2
Before connectingCharge the headset, wear it correctly, and close the Muse mobile app so only this setup can connect.
Open BlueMuse
Waiting for streamConnect Muse 2 in BlueMuse on this Windows computer, then start the EEG/LSL stream from BlueMuse.
Start the website connector
Not connectedClick Start Muse connector below. MonmonAI uses your logged-in account and this page's EEG session automatically.
Confirm consent
Needs consentTick the consent box below. EEG-style signals are optional and used only as learning support, not medical feedback.
Create the session
Create sessionClick New EEG session so the website has a session to attach incoming Muse 2 events to.
Record baseline
Needs baselineRecord rest and task baseline samples, then derive the baseline before starting the learning session.
Start learning
Ready after baselineClick Start, keep the connector running, then continue learning while Muse 2 sends optional focus context.
Live headset status
Muse 2 is not connected yet.
Start BlueMuse streaming, then click Start Muse connector. Brainwave and mental-state indicators appear after the first live event arrives.
No service status yet
0 event(s) received
Waiting for focus score
Waiting for attention score
Session and consent
Start with consent, baseline, then learning.
EEG focus is compared against this learner's baseline when available. Bad contact, blinks, jaw clench, and movement are flagged before the signal is used for learning pressure.
Guided baselineReady to calibrate.
Use guided baseline to collect rest and task samples automatically.
30s x 2Connect Muse before relying on this baseline
Task baseline reading text
Read this carefully: The learner reviews a new word, connects it to meaning, and recalls it in a short sentence. Stay relaxed, keep the headset still, and read at a normal pace.
Word difficulty signal
Attach focus and attention to a saved word.
Low attention or high difficulty marks the word harder and pulls its review timing earlier.
Words with low focus
Review words that synthetic EEG marked as difficult.
Run the no-headset demo or send word-linked Muse events to build this list.
Privacy and retention
Control stored EEG learning signals.
EEG-style data is treated as optional learning interaction data. You can export it, apply a retention window, or delete stored Muse sessions and events.