Science Behind Our Focus Tracking
Understanding EEG
The Muse 2 headband uses EEG (electroencephalography) to monitor brain activity. It records electrical signals from your brain, providing insights into your mental state, such as whether you’re calm, focused, or distracted. However, EEG data can be quite noisy, which means it’s not precise enough to pinpoint exact thoughts or actions. Instead, it gives an overall sense of your mental state.
Think of it like being outside a football stadium during a game. You can't see the exact plays or know who has the ball, but you can tell when a team scores or when the crowd is excited. Similarly, while the Muse 2 can’t tell you every detail of your brain’s activity, it can highlight key shifts in your mental state. Our software analyzes this data to help you discover the optimal conditions for your work, providing actionable insights based on the unique traits of your brain.
Minimal Signal Processing
We apply only essential filtering and bad channel interpolation to preserve the integrity of your brain signals, avoiding complex corrections that could degrade data quality (Delorme et al., 2023).
Faulty Signal Removal
We identify and exclude excessively noisy segments to ensure only the cleanest data is used for analysis. We use a simplified version of the SPEED architecture introduced in (Gjølbye et al., 2024) .
Feature Encoding with NeuroGPT
We employ NeuroGPT, a model designed specifically for EEG data, which extracts meaningful patterns using self-supervised learning to predict masked data segments (Cui et al., 2024) .
Personalized Classification and Insights
The system analyzes your sessions in windows of 2-second chunks to evaluate your focus levels. It continuously adapts to your unique EEG patterns.
Continuous Learning and Adaptation
As you use the service, the system learns and evolves, providing increasingly tailored insights into your ideal environment for focus and productivity. EEG is personal, and our approach ensures the insights are specific to you, especially for deep work tasks.
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