- Remove the current class from the content27_link item as Webflows native current state will automatically be applied.
- To add interactions which automatically expand and collapse sections in the table of contents select the content27_h-trigger element, add an element trigger and select Mouse click (tap)
- For the 1st click select the custom animation Content 27 table of contents [Expand] and for the 2nd click select the custom animation Content 27 table of contents [Collapse].
- In the Trigger Settings, deselect all checkboxes other than Desktop and above. This disables the interaction on tablet and below to prevent bugs when scrolling.
We all want better focus, and we’ve built the headphones to help you get there. But at Neurable, we know that "smart" technology is only as good as the science behind it.
For years, high-quality brain sensing was stuck in research labs. The "gold standard" equipment uses "wet" electrodes—sensors that require sticky, messy conductive gel to work. They are accurate, but you definitely wouldn't want to wear them on your morning commute. Neurable’s goal was to take that high-quality data and pack it into a comfortable, everyday pair of headphones using "dry" fabric sensors.
But can a comfortable set of headphones really compete with medical-grade lab equipment?
To find out, we teamed up with the U.S. Air Force Research Laboratory (AFRL). They put our Enten™ headphones through the wringer, comparing them directly against traditional research gear. Here is what they found.
1. No Gel? No Problem.
The biggest skepticism about consumer brain-sensing devices is usually signal quality. Dry sensors are convenient, but they have historically struggled to be as precise as the messy, wet ones.
In the first study, researchers had participants wear both the Neurable Enten headphones and a medical-grade, gel-based headset at the same time to compare the data.
The Verdict: Neurable held its own.
- Medical-Grade Accuracy: When looking at brainwave power, the Enten headphones recorded signal strengths similar to the heavy-duty research headset.
- Smart Cleaning: Raw data from dry sensors can be noisy, but the study showed that Neurable’s proprietary cleaning algorithm effectively tidied up the signal, making key brainwave features (like Alpha peaks) stand out clearly.
- Reliable Data: When analyzing specific neural features (like center frequency), there was no significant difference between our headphones and the wet electrode system.
Basically, you get the lab-quality data without the lab-quality mess.
2. The "Focus Score" Isn't Just a Gimmick
We show you a "Focus Score" in our app, but does that number actually reflect what's happening in your brain? The Air Force tested this by asking: Can Neurable’s Focus algorithm actually tell when you are distracted?
In the second study, participants performed a demanding cognitive task (the Stroop test) while being randomly interrupted to solve math problems.
The Verdict: The Focus Score is the real deal.
- It Sees Distraction: When participants were interrupted, their Focus Scores didn’t just stay flat—it dropped. The study showed the algorithm successfully captured these distraction events.
- The Cost of Interruptions: The data showed that after a distraction, it took people about 20 to 30 seconds to mentally "recover" and get their focus back to where it was.
- Better Focus = Better Performance: Most importantly, the study proved that a high Focus Score predicts real-world performance. When users had higher focus scores, they had significantly faster reaction times when answering correctly.
The Bottom Line
This collaboration with the Air Force Research Lab was a major validation for us. It proved that our hardware can compete with traditional medical gear and that our software accurately tracks your cognitive state.
2 Distraction Stroop Tasks experiment: The Stroop Effect (also known as cognitive interference) is a psychological phenomenon describing the difficulty people have naming a color when it's used to spell the name of a different color. During each trial of this experiment, we flashed the words “Red” or “Yellow” on a screen. Participants were asked to respond to the color of the words and ignore their meaning by pressing four keys on the keyboard –– “D”, “F”, “J”, and “K,” -- which were mapped to “Red,” “Green,” “Blue,” and “Yellow” colors, respectively. Trials in the Stroop task were categorized into congruent, when the text content matched the text color (e.g. Red), and incongruent, when the text content did not match the text color (e.g., Red). The incongruent case was counter-intuitive and more difficult. We expected to see lower accuracy, higher response times, and a drop in Alpha band power in incongruent trials. To mimic the chaotic distraction environment of in-person office life, we added an additional layer of complexity by floating the words on different visual backgrounds (a calm river, a roller coaster, a calm beach, and a busy marketplace). Both the behavioral and neural data we collected showed consistently different results in incongruent tasks, such as longer reaction times and lower Alpha waves, particularly when the words appeared on top of the marketplace background, the most distracting scene.
Interruption by Notification: It’s widely known that push notifications decrease focus level. In our three Interruption by Notification experiments, participants performed the Stroop Tasks, above, with and without push notifications, which consisted of a sound played at random time followed by a prompt to complete an activity. Our behavioral analysis and focus metrics showed that, on average, participants presented slower reaction times and were less accurate during blocks of time with distractions compared to those without them.
.webp)