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Biomedical devices

Biomedical devices

Drowsiness-detecting earbuds could help drivers stay safe at the wheel

22 Aug 2024 Tami Freeman
In-ear device detects signs of drowsiness
Tracking brain activity Researchers at UC Berkeley are developing in-ear devices that can be worn throughout the day to record neural signals from inside the ear canal and detect signs of drowsiness. (Courtesy: CC BY/Nat. Commun. 10.1038/s41467-024-48682-7)

Drowsiness plays a major role in traffic crashes, injuries and deaths, and is considered the most critical hazard in construction and mining. A wearable device that can monitor fatigue could help protect drivers, pilots and machine operators from the life-threatening dangers of fatigue.

With this aim, researchers at UC Berkeley are developing techniques to detect signs of drowsiness in the brain, using a pair of prototype earbuds to perform electroencephalography (EEG) and other physiological measurements. Describing the device in Nature Communications, the team reports successful tests on volunteers.

“Wireless earbuds are something we already wear all the time,” says senior author Rikky Muller in a press statement. “That’s what makes ear EEG such a compelling approach to wearables. It doesn’t require anything extra. I was inspired when I bought my first pair of Apple’s AirPods in 2017. I immediately thought, ‘What an amazing platform for neural recording’.”

Improved design

EEG uses multiple electrodes placed on the scalp to non-invasively monitor the brain’s electrical activity – such as the alpha waves that increase when a person is relaxed or sleepy. Researchers have also demonstrated that multi-channel EEG signals can be recorded from inside the ear canal, using in-ear sensors and electrodes.

Existing in-ear devices, however, mostly use wet electrodes (which necessitate skin-preparation and hydrogel on the electrodes), contain bulky electronics and require customized earpieces for each user. Instead, Muller and colleagues aimed to create an in-ear EEG with long-lifespan dry electrodes, wireless electronics and a generic earpiece design.

In-ear EEG device

The researchers developed a fabrication process based on 3D printing of a polymer earpiece body and electrodes. They then plated the electrodes with copper, nickel and gold, creating electrodes that remain stable over months of use. To ensure comfort for all users, they designed small, medium and large earpieces (with slightly different electrode sizes to maximize electrode surface area).

The final medium-sized earpiece contains four 60 mm2 in-ear electrodes, which apply outward pressure to lower the electrode–skin impedance and improve mechanical stability, plus two 3 cm2 out-ear electrodes. Signals from the earpiece are read out and transmitted to a base station by a low-power wireless neural recording platform (the WANDmini) affixed to a headband.

Drowsiness study

To assess the earbuds’ performance, the team recorded 35 h of electrophysiological data from nine volunteers. Subjects wore two earpieces and did not prepare their skin beforehand or apply hydrogel to the electrodes. As well as EEG, the device measured signals such as heart beats (using electrocardiography) and eye movements (via electrooculography), collectively known as ExG.

To induce drowsiness, subjects played a repetitive reaction time game for 40–50 min. During this task, they rated their drowsiness every 5 min on the Karolinska Sleepiness Scale (KSS). The measured ExG data, reaction times and KSS ratings were used to generate labels for classifier models. Data were labelled as “drowsy” if the user reported a KSS score of 5 or higher and their reaction time had more than doubled since the first 5 min.

To create the alert/drowsy classifier, the researchers extracted relevant temporal and spectral features in standard EEG frequency bands (delta, theta, alpha, beta and gamma). They used these data to train three low-complexity machine learning models: logistic regression, support vector machines (SVM) and random forest. They note that spectral features associated with eye movement, relaxation and drowsiness were the most important for model training.

All three classifier models achieved high accuracy, with comparable performance to state-of-the-art wet electrode systems. The best-performing model (utilizing a SVM classifier) achieved an average accuracy of 93.2% when evaluating users it had seen before and 93.3% with never-before-seen users. The logistic regression model, meanwhile, is more computationally efficient and requires significantly less memory.

The researchers conclude that the results show promise for developing next-generation wearables that can monitor brain activity in work environments and everyday scenarios. Next, they will integrate the classifiers on-chip to enable real-time brain-state classification. They also intend to miniaturize the hardware to eliminate the need for the WANDmini.

“We plan to incorporate all of the electronics into the earbud itself,” Muller tells Physics World. “We are working on earpiece integration, and new applications, including the use of earbuds during sleep.”

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