The Dreem 3S headband collects PSG-quality EEG data from anywhere—at home, throughout the day, or in clinical settings—without disrupting sleep.
Dreem 3S
PSG-quality data in ecologically valid settings
Battery
- Maximum charging time: 3 hours and 30 minutes
- Average charging time: 1 hour of charging for up to 8 hours of recording
Storage
- Maximum number of recordings on memory: 24 hours
- Maximum recording duration: 24 hours
Output
- EEG Channels: 5
- Sleep endpoints: TST, WASO, N1, N2, N3, REM sleep, SOL, number and duration of awakenings
- Raw data: EDF
EEG sensors:
- 2 frontal sensors in F7 and F8 locations to measure frontal brain activity
- 1 ground sensor on the frontal band (Fp2 location)
- 2 occipital sensors on O1 and O2 locations to monitor occipital brain activity
Accelerometer:
- 3D accelerometer to measure movements, head position, and respiratory rate/trace during sleep
Power
- Power button
- Magnetic port for charging
Scalable Brain Monitoring
A validated alternative to polysomnography (PSG)
Simple, user-friendly setup
Beacon Pal is a mobile companion app that pairs seamlessly with the Dreem 3S headband using Bluetooth technology, guiding patients and caregivers through easy at-home setup to begin EEG data collection.
User Centric
Enables patient-initiated recording, making it easy to start and end sessions with one tap
Customizable
Supports six different languages and offers built-in customizable questionnaires for patient-reported assessments
Secure
Collects EEG data through a secure, encrypted, wireless connection with version-controlled deployment
Instructive
Ensures that the headband is correctly placed on participants prior to data collection to catch user errors in real time
Peer-reviewed validation
This independent and peer-reviewed study shows that the Dreem 3S headband achieves a level of measurement as accurate as polysomnography (PSG), and sleep stage analysis capabilities that match or exceed those of human experts.
Find answers to common questions about the Dreem 3S headband, data access, study setup, and more.
Brought to you by Dreem 3S
Our Publications
Arnal PJ et al. SLEEP.
The Dreem Headband compared to polysomnography for electroencephalographic signal acquisition and sleep staging
Chambon et al.
DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal
Chambon et al.
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series
Waeber et al.
Acoustic stimulation time-locked to the beginning of sleep apnea events reduces oxygen desaturations: a pilot-study
Debellemaniere et al.
Performance of an Ambulatory Dry-EEG Device for Auditory Closed-Loop Stimulation of Sleep Slow Oscillations in the Home Environment
Thorey et al.
The dreem2 headband as an alternative to polysomnography for eeg signal acquisition, breathing and heart rate monitoring and sleep staging in healthy subjects technology/technical