Healthcare (Commonwealth Union) – Drowsy driving leads to hundreds of fatal vehicle accidents annually in the U.S., with the National Safety Council identifying fatigue as a significant risk in construction and mining sectors.
To mitigate these risks, engineers at the University of California, Berkeley, have developed prototype earbuds that can monitor brain signs of drowsiness and form a driver alert together with alerts for heavy machine operators and those at of their work posing a risk when fatigued.
These earbuds function similarly to an electroencephalogram (EEG), a diagnostic tool used to measure brain electrical activity. Unlike traditional EEGs, which use multiple electrodes placed on the scalp, the earbuds employ built-in electrodes that fit into the ear canal.
While the electrical signals captured by these earbuds are less intense compared to those of a conventional EEG, recent research demonstrates that the Ear EEG platform is sufficiently sensitive to detect alpha waves, a type of brain activity associated with drowsiness and the onset of sleep.
“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,’” added the study senior author Rikky Muller, who is an associate professor of electrical engineering and computer sciences at UC Berkeley. “We believe that this technology has many potential uses, and that classifying drowsiness is a good indicator that the technology can be used to classify sleep and even diagnose sleep disorders.”
With the application of an earbud as an EEG electrode presents several practical difficulties. To achieve an accurate EEG reading, the electrodes must establish a good connection with the skin. This is relatively straightforward with traditional EEGs, which employ flat metal electrodes adhered to the scalp. The researchers indicated however, that designing an earbud that fits securely and comfortably across various ear shapes and sizes is considerably more challenging.
When Muller’s team began their project, other groups working on Ear EEG platforms were either using wet electrode gels to ensure a proper seal between the earbud and the ear canal or crafting custom-molded earpieces for each user. Muller and her team aimed to create a model that was dry and universally applicable, allowing anyone to insert them into their ears and obtain reliable readings.
“My personal goal was to try to make a device that could be used every day by someone who would really benefit from it,” added Ryan Kaveh, who is a UC Berkeley postdoctoral scholar and co-first author of the study. “In order to do that, I knew that it would have to be reusable, fit a variety of people, and [be] easy to manufacture.”
Kaveh, together with graduate student Carolyn Schwendeman, led the study and worked with Ana Arias’s lab at UC Berkeley to develop the final version of the earpiece, available in three sizes: small, medium, and large. This earpiece features multiple electrodes arranged in a cantilevered design that applies gentle outward pressure to the ear canal, with flexible electronics for a comfortable fit. It uses a custom, low-power, wireless electronic interface to read the signals.
In a 2020 paper, the researchers demonstrated that these earpieces could detect various physiological signals, such as eye blinks, alpha brain waves, and the auditory steady-state response, which is the brain’s reaction to a constant pitch. In the latest study, they refined the earpiece design and utilized machine learning to show its potential for practical applications.
For the experiment, nine volunteers wore the earpieces while performing a series of monotonous tasks in a dark room. Periodically, they were asked to assess their drowsiness levels, and their response times were recorded.
Muller, who created the Ear EEG with the aid of the Bakar Fellowship and the Bakar Prize, is still enhancing the device’s design and investigating additional possible uses. This device not only records EEG signals but can also capture other physiological signals, such as heartbeats, eye movements, and jaw clenching.
An anti-drowsy technology to bring about a driver fatigue monitoring system. Innovative earbuds to detect drowsiness may save many lives, particularly those working long shifts.
