A comprehensive Python application for analyzing psychometric, vitals, and neurological imaging data. This application provides a robust pipeline for data ingestion, preprocessing, feature extraction, correlation analysis, and machine learning modeling.
This pipeline enables researchers and data scientists to process and analyze multi-modal data from various sources including psychometric assessments, physiological measurements, and neurological imaging. The system automates the entire workflow from raw data ingestion through analysis and visualization.
- Multi-modal data processing support:
- Eye tracking data (fixations, saccades, pupil dilation)
- EEG data (raw signals, frequency bands, ERPs)
- Survey responses (psychometric scales, questionnaires)
- Vital signs (heart rate, GSR, breathing rate)
- Face heat maps (thermal imaging data)
- Motion capture data
- Voice recordings
- Automated data preprocessing and cleaning
- Artifact removal
- Signal filtering
- Missing data imputation
- Outlier detection
- Advanced feature extraction
- Time-domain features
- Frequency-domain features
- Statistical features
- Cross-modal features
- Statistical analysis
- Correlation analysis
- Factor analysis
- Time series analysis
- Dimensionality reduction
- Machine learning capabilities
- Supervised learning models
- Unsupervised clustering
- Deep learning integration
- Cross-validation
- Visualization tools
- Interactive plots
- 3D visualizations
- Time series plots
- Statistical charts
- Production features
- Comprehensive logging
- Error handling
- Progress tracking
- Performance optimization
- Parallel processing
- Configurable pipeline parameters
- Modular, extensible architecture
- Python 3.8+
- CUDA-capable GPU (optional, for deep learning)
- 16GB+ RAM recommended
- 100GB+ storage space for data
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