NeuroFeel
Bridging laboratory precision with real-world emotion recognition through advanced domain adaptation
Our revolutionary framework brings together physiological sensing and machine learning to deliver consistent emotion recognition across different environments and datasets.
Personalization Accuracy
88.16%
Domain Gap Reduction
89%
WESAD Personalization
Our personalized approach delivers exceptional accuracy through adaptive model selection, achieving 88.16% accuracy in discrete emotion recognition.
Cross-Dataset Transfer
Our pioneering framework bridges laboratory and real-world environments, achieving 80.27% valence recognition accuracy across different datasets.
Revolutionizing Emotion Recognition Across Environments
NeuroFeel tackles the fundamental challenge of making emotion recognition systems work consistently across different environments and datasets
Our framework systematically combines WESAD (lab-controlled) and K-EmoCon (in-the-wild) datasets, enabling emotion recognition across different data collection contexts.
Our novel ensemble approach combines CORAL, subspace alignment, and feature scaling to significantly reduce the domain gap between laboratory and real-world settings.
Our framework uniquely provides comprehensive bidirectional evaluation, allowing models to be trained on one dataset and tested on another in both directions.
The Science Behind NeuroFeel
Two complementary frameworks that represent the future of emotion recognition technology
WESAD Personalization
Adaptive Model Selection
Dynamically selects between base and personal models based on confidence thresholds
Four-Model Architecture
Base, Personal, Ensemble, and Adaptive models working in harmony
Minimal Calibration
Achieves high accuracy with very limited personalization data
Adaptive Selection Architecture
Breakthrough Research Results
NeuroFeel represents a significant advancement in emotion recognition technologies
Our adaptive personalization framework achieved 88.16% accuracy with a +1.80% improvement over base models, demonstrating the effectiveness of our confidence-based selection approach.
Our ensemble adaptation approach successfully reduced the domain gap by 89% between laboratory and real-world datasets, with significant performance improvements in cross-dataset emotion recognition.
We identified key physiological features that transfer well between datasets, with ECG/HR features showing consistently high importance for cross-dataset emotion recognition.
Applications & Future Directions
Practical Applications
- Wearable emotion sensing devices with consistent performance
- Mental health applications with reduced calibration requirements
- Affective computing systems with improved generalizability
- Human-computer interaction with consistent emotion recognition
Future Research Directions
- Expanding to additional physiological datasets and sensor modalities
- Integrating visual and audio emotion cues with physiological signals
- Enhancing domain adaptation with self-supervised approaches
- Developing real-time adaptation for continuous emotion monitoring
Experience NeuroFeel in Action
Explore our interactive demonstration to see domain adaptation and personalization in real-time
Live Framework Demo
Our interactive demo allows you to explore both frameworks side by side, comparing performance and visualizing the domain adaptation process in real-time.
Foundational Datasets
NeuroFeel builds upon these key emotion recognition datasets
WESAD is a multimodal dataset for wearable stress and affect detection featuring physiological and motion data recorded from both wrist and chest-worn devices in a controlled laboratory environment.
K-EmoCon is a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations, featuring audiovisual recordings, EEG, and peripheral physiological signals.
Ready to Experience the Future of Emotion Recognition?
Explore our interactive demos and see how NeuroFeel bridges the gap between laboratory precision and real-world applications in emotion recognition technology