- 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.
Brain data is one of those phrases that can sound either compelling or alarming, depending on how it's framed. So let's be concrete: what does brain data actually tell you, what can you do with it, and how reliable is it?
The basics: what brain data measures
When we talk about brain data in the context of consumer wearables, we're primarily talking about EEG — the electrical signals produced by neural activity, measured through sensors on the scalp.
These signals aren't binary. They're continuous, dynamic, and rich with information about your current cognitive state. Some of the core metrics that can be reliably derived from EEG in a consumer context are:
- Focus: the degree to which your attention is concentrated on a task, versus wandering or divided
- Fatigue: the accumulation of cognitive effort over time, and its effect on processing efficiency
- Mental workload: the total cognitive demand being placed on your brain at a given moment
What this means in practice
Take focus as an example. Most people have a vague sense of when they're focused and when they're not. But that intuition is imprecise and often wrong — particularly as fatigue accumulates. You may feel like you're working when you're actually on autopilot. You may feel distracted when you're actually processing deeply (or not realize you’re minutes away from fading).
A continuous, objective focus signal changes this. It lets you see patterns you can't observe subjectively: when your peak focus window actually occurs during the day, how long you can sustain deep focus before it degrades, how specific environments or tasks affect your cognitive state.
For enterprises and the defense world, the value is different but equally concrete. Knowing the cognitive state of a team — in aggregate and in real time — enables smarter workflow design, earlier identification of fatigue risk in high-stakes roles, and evidence-based decisions about rest, workload distribution, and environment.
The reliability question
The reasonable skeptic question is: how reliable is this, really?
The honest answer is that it depends on the data and the AI. EEG signals are noisy and highly individual — what focus looks like in one person's brainwaves can differ significantly from another's. Early consumer EEG products struggled with this, producing outputs that were inconsistent enough to undermine trust.
The key variable is the size and diversity of the training dataset. AI models trained primarily on lab data — clean signals from a controlled population — generalize poorly to real-world use. Models trained on large, diverse datasets of real-world EEG produce meaningfully better results.
This is Neurable's core technical advantage: our AI is trained on the world's largest real-world EEG dataset, collected from actual consumer use across diverse populations and environments. The result is cognitive state classification that holds up outside a lab — in offices, during workouts, on commutes, over the course of a full day.
What brain data can't tell you (yet)
It's worth being clear about the limits. Current consumer-grade EEG cannot read thoughts, diagnose clinical conditions, or predict behavior. It measures cognitive performance and health — the functional condition of your brain in real time — not content.
It also cannot replace clinical assessment for anything medical. It can aid them, but cognitive wearables are performance tools, not diagnostic devices.
The appropriate frame is this: brain data tells you how your brain is running, not what it's thinking. That's a meaningful distinction — and it's also a genuinely useful category of information that didn't exist in accessible, real-time form until very recently.
The bottom line
Brain data, done well, gives you a real-time window into your cognitive state that no other signal can provide. It's not a perfect picture, and it has limits. But it's the most direct measure of mental performance available outside a clinical setting — and it's now accessible through hardware people already want to wear.
The question for most consumers won't be whether they want this information. It will be which device delivers it in a form they actually trust and use.
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.
