The researchers say the device could prove valuable among pilots, healthcare workers and other professions where managing mental workload is crucial to preventing catastrophes.
The team found that as the task became harder, the different types of brainwave detected showed shifts in activity that corresponded to a greater mental workload.
After a training period, the researchers found the algorithm did better than chance at predicting the mental workload of a participant based on their EEG and EOG data alone.
The team are now developing the approach so signals can be decoded by the device’s microprocessor and sent to an app to alert the user if their mental workload is too high.
“Previous studies indicated that the optimal mental performance occurs when the mental workload demand is not too low or too high,” said Lu.
Doing math or figuring out what to text your new date are two examples of tasks that make people frown. Scientists claim to have developed a tool to track such efforts: an electronic tattoo applied to the forehead.
The gadget may be useful for pilots, medical professionals, and others in occupations where controlling mental strain is essential to averting disasters, according to the researchers.
According to Dr. Nanshu Lu, a research author from the University of Texas at Austin, “eventually we hope to have this real-time mental workload decoder that can give people some warning and alert so that they can self-adjust, or they can ask AI or a co-worker to offload some of their work.” The device may not only help workers avoid serious mistakes but also protect their health in such high-demand and high-stakes situations.
In their article in the journal Device, Lu and colleagues discuss the drawbacks of using questionnaires to measure mental workload, including the fact that they are typically administered after a task and that people are not very good at objectively assessing cognitive effort.
In the meantime, current electroencephalography (EEG) and electrooculography (EOG) devices, which measure brain waves and eye movements, respectively, to evaluate mental workload, are wired, heavy, and prone to inaccurate readings from motion.
The “e-tattoo” is a wireless, flexible, and lightweight gadget in contrast.
The e-tattoo’s black, wiggly path is made of conductive material based on graphite and is affixed to the forehead with conductive adhesive film.
Rectangular EOG electrodes placed vertically and horizontally around the eyes provide information about eye movements, while four square EEG electrodes placed on the forehead each identify a distinct area of brain activity, with a reference electrode behind the ear. Every stretchable electrode has a layer of extra conductive material applied to it.
A lightweight battery can be clipped to the disposable, custom-made e-tattoo, which is connected to a reusable flexible printed circuit via conductive tape.
The researchers tested the e-tattoo on six participants after discovering that it performed as well as conventional EEG and EOG devices for tracking eye movements and brain waves.
A screen with 20 letters flashing up at different locations, one at a time, was displayed to each participant. After a specified number of letters (N) were displayed, participants were instructed to click with their mouse if the letter or its location matched one displayed. The task was completed by each participant several times, with the N value fluctuating between 0 and 3, which represents four levels of difficulty.
The researchers discovered that the various brainwave types they identified exhibited changes in activity in response to increasing mental workload as the task got more difficult.
Along with the “N” numbers, the team then entered the EEG and EOG data into a machine-learning algorithm. The algorithm outperformed chance at predicting a participant’s mental workload based solely on their EEG and EOG data after a training period, the researchers discovered.
According to Lu, the entire device—including the chip and battery—should cost less than $200 (£148). The group is currently working on a method that will allow the device’s microprocessor to decode signals and send them to an app, which will notify the user if their mental workload becomes too heavy.
However, switching to a simple task isn’t always the solution.
According to Lu, “prior research showed that the best mental performance happens when the mental workload demand is neither too low nor too high.”. It’s extremely dull and causes people to lose focus when it’s too low. “.”.
