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An open-source wearable platform to enhance human cognitive functions and well-being
Highly-sensitive multimodal head-based biosensing
Highly-sensitive multimodal head-based biosensing
Our NeuralWear1.0 takes the form factor of an ear-worn wearable that wraps around the ears like a hearing aid. It can capture important head-based biosignals, including.
Brain waves (Electroencephalogram, EEG).
Eye movements (Electrooculography, EOG).
Facial muscle contractions (Electromyography, EMG).
Galvanic skin responses (GSR).
Heart rate and heart rate variability (HRV).
Motions (Accelerations, orientation and angular velocity).
Noise and motion robustness
Noise and motion robustness
Our NeuralWear1.0's biosensing earpieces (EEG/EOG/EMG) have been proven robust to environmental noise and motion artefacts, thanks to our novel designs featuring active electrodes and multi-stage pre-amplification directly at the sensing sources:
Ultra-high input impedance (10TΩ): 1000-10000X higher than state-of-the-art EEG recorders (e.g. ANTNeuro, OpenBCI) or conventional medical standard (>100MΩ). This ensures the highest signal fidelity and minimal coupled electromagnetic noise.
100X on-electrode pre-amplification: tiny uV biosignals are amplified directly on the electrode by using a novel feed-forward differential amplification method. The output biosignals are 100X amplified and converted to differential mode to ensure robustness over transmission wires.
Wireless real-time streaming
Wireless real-time streaming
Our NeuralWear1.0 wirelessly streams captured biosignals to a computer or mobile device over Bluetooth for real-time visualisation and processing. We provide a customised driver for our NeuralWear to work directly with the OpenVIBE framework.
On-device AI
On-device AI
Our NeuralWear1.0 features a tinyML framework and a neural accelerator to efficiently run embedded AI models directly on the device. The NeuralWear1.0 adopts Arduino NICLA Voice as the main controller unit with an on-board Syntiant NDP120 AI accelerator.
Demonstrations & Use-Cases
Demonstrations & Use-Cases
Neural real-time biosensing demo and tutorial with OpenVibe
Neural real-time biosensing demo and tutorial with OpenVibe
Brainwave sensing and drone control demo
Brainwave sensing and drone control demo
Scientific Studies
Scientific Studies
(1) An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection.
(1) An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection.
ACM Transactions on Computing for Healthcare 2025 (IF: 8.0)
ACM Transactions on Computing for Healthcare 2025 (IF: 8.0)
Aziz, Abdul, Nhat Pham, Neel Vora, Cody Reynolds, Jaime Lehnen, Pooja Venkatesh, Zhuoran Yao et al.
Aziz, Abdul, Nhat Pham, Neel Vora, Cody Reynolds, Jaime Lehnen, Pooja Venkatesh, Zhuoran Yao et al.
Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel li,ghtweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing-computing-communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer/mobile phone nearby, where the data is uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies.
Epilepsy is one of the most common neurological diseases globally (around 50 million people worldwide). Fortunately, up to 70% of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel li,ghtweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing-computing-communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer/mobile phone nearby, where the data is uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies.
(2) Detection of Microsleep Events with a Behind-the-ear Wearable System
(2) Detection of Microsleep Events with a Behind-the-ear Wearable System
IEEE Transactions on Mobile Computing 2023 (IF=7.9)
IEEE Transactions on Mobile Computing 2023 (IF=7.9)
ACM MobiSys 2020 (Acceptance rate: 19.4%)
ACM MobiSys 2020 (Acceptance rate: 19.4%)
N. Pham, T. Dinh, Z. Raghebi, T. Kim, N. Bui, P. Nguyen, H. Truong, F. Banaei-Kashani, A. Halbower, T. Dinh, and T. Vu
N. Pham, T. Dinh, Z. Raghebi, T. Kim, N. Bui, P. Nguyen, H. Truong, F. Banaei-Kashani, A. Halbower, T. Dinh, and T. Vu
WAKE is a novel wearable device that detects microsleep by monitoring biosignals from the brain, eye movements, facial muscle contractions, and sweat gland activities from behind the user’s ears. To ensure real-world reliability, WAKE introduces a Three-fold Cascaded Amplifying technique to tame motion artefacts and environmental noise. We evaluate the system with our custom-built prototype on 19 subjects and obtain 76% precision and 85% recall, showing the feasibility of microsleep detection on unseen subjects.
WAKE is a novel wearable device that detects microsleep by monitoring biosignals from the brain, eye movements, facial muscle contractions, and sweat gland activities from behind the user’s ears. To ensure real-world reliability, WAKE introduces a Three-fold Cascaded Amplifying technique to tame motion artefacts and environmental noise. We evaluate the system with our custom-built prototype on 19 subjects and obtain 76% precision and 85% recall, showing the feasibility of microsleep detection on unseen subjects.
Collaborators
Collaborators
We want to thank all our collaborators who have worked with us to make this technology happen. The effort to open source the platform at Cardiff University was generously funded by Google Research through the Google Research Scholar Award 2025.
Available resources
Available resources
Our source code and tutorials are available at our lab's GitHub repo: https://github.com/AgileCPS/neuralwear, under the MIT license.
Publications
Publications
[1] Over-the-ear Biosensing Wearables
ACM GetMobile 2025 - Mobile Computing and Communications
Nhat Pham, Nam Bui, Phuc Nguyen.
[2] An Unobtrusive and Lightweight Ear-worn System for Continuous Epileptic Seizure Detection
ACM HEALTH 2025 - ACM Transactions on Computing for Healthcare
Abdul Aziz, Nhat Pham, Neel Vora, Cody Reynolds, Jaime Lehnen, Pooja Venkatesh, Zhuoran Yao, Jay Harvey, Tam Vu, Kan Ding, Phuc Nguyen.
[3] Stress-GPT: Towards stress detection with an EEG-based foundation model
ACM MobiCom 2024 Workshop - International Workshop on Mobile Computing with Efficient and Interactive Foundation Models
Catherine Lloyd*, Loic Lorente Lemoine*, Reiyan Al-Shaikh, Kim Tien Ly, Hakan Kayan, Charith Perera, Nhat Pham. (* co-first authors)
[4] Detection of Microsleep Events with a Behind-the-ear Wearable System.
IEEE TMC 2023 - IEEE Transactions on Mobile Computing, Vol. 22, Iss. 2, Feb. 2023 (IF: 7.9).
Nhat Pham, Tuan Dinh, Taeho Kim, Zohreh Raghebi, Nam Bui, Hoang Truong, Tuan Nguyen, Farnoush Banaei-Kashani, Ann Halbower, Thang Dinh, Phuc Nguyen, and Tam Vu.
ACM MobiCom 2022 - The 28th ACM Intl’ Conf. on Mobile Computing and Networking.
Nhat Pham, Hong Jia, Minh Tran, Tuan Dinh, Nam Bui, Young Kwon, Dong Ma, Phuc Nguyen, Cecilia Mascolo, Tam Vu.
[6] WAKE: A Behind-the-ear Wearable System for Microsleep Detection.
ACM MobiSys 2020 - The 18th ACM Intl' Conf. on Mobile Systems, Applications, and Services.
Nhat Pham, Tuan Dinh, Zohreh Raghebi, Taeho Kim, Nam Bui, Phuc Nguyen, Hoang Truong, Farnoush Banaei-Kashani, Ann Halbower, Thang Dinh, and Tam Vu.
ACM MobiSys 2019 - 17th ACM International Conference on Mobile Systems, Applications, and Services.
Nhat Pham, Taeho Kim, Frederick M Thayer, Anh Nguyen, and Tam Vu.
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