Real-time EEG Stress Detection
Monitor your stress levels in real-time using advanced EEG technology and machine learning
Stress Level
23%
Monitor your stress levels in real-time using advanced EEG technology and machine learning
Understanding the challenges of stress monitoring
Electroencephalography (EEG) signals are complex and require sophisticated analysis to extract meaningful stress indicators from the noise.
Traditional stress detection methods are slow and don't provide immediate feedback when stress levels change and how they impact productivity during task requring signifigant concentration like studying.
Many stress detection systems lack the accuracy needed for reliable readings. Our SVM-based algorithm has achived 97% in similar studies.
Professional EEG equipment is expensive and not accessible for everyday stress monitoring at home or work. We aim to develpe a discrete and wearble EEG headset in the form of headphones.
Gauge combines cutting-edge EEG technology with advanced machine learning algorithms to provide real-time, accurate stress detection.
Current Stress Level
Gauge what makes Gauge unique
Advanced Algorithms process EEG signals in real-time to detect stress patterns with high accuracy.
Comprehensive analytics and insights into your stress patterns over time with detailed reports.
Receive notifications when stress levels exceed healthy thresholds with personalized recommendations.
Monitor stress levels across teams and organizations for better workplace wellness management.
Experience real-time stress detection with our interactive demo
Low Stress
Our roadmap for revolutionizing stress monitoring and wellness technology
Develop individual stress baselines and patterns using machine learning to create personalized stress management recommendations.
Implement predictive analytics to forecast stress episodes before they occur, enabling proactive stress management.
Measure and analyze the effectiveness of different stress reduction techniques for each individual.
Implement quantum support vector machines (QSVM) for enhanced pattern recognition and faster processing.
Develop convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for advanced EEG signal processing.
Implement on-device processing to reduce latency and improve privacy while maintaining accuracy.
Implement sophisticated signal processing algorithms to improve EEG signal quality and reduce artifacts.
Combine EEG data with other biometric signals for comprehensive stress assessment.
Develop intelligent systems to detect and remove motion artifacts and other signal contaminants in real-time.
Employee stress monitoring and wellness programs
Student stress management and academic performance
Clinical stress assessment and mental health monitoring
Real-time stress feedback for immersive experiences
Ready to revolutionize stress monitoring?
nearhos.hatzinikolaou22@gmail.com
+1 (416) 871-6136
Waterloo, ON