I develop and maintain various software tools and analysis scripts for neuroscience research, with a focus on EEG/ECG signal processing and data analysis automation.
Signal Processing Tools
EEG Analysis Pipeline
Language: MATLAB/Python
Purpose: Automated preprocessing and analysis of EEG data
Complete EEG preprocessing and analysis pipeline workflow
Features:
- Automated artifact detection and removal
- ICA-based eye movement correction
- Time-frequency decomposition
- Event-related potential (ERP) analysis
- Quality control metrics and reporting
ECG Processing Suite
Language: Python
Purpose: Cardiac signal analysis and heart rate variability
EKG peak detection and correction interface for heart rate variability analysis
Features:
- R-peak detection algorithms
- HRV parameter extraction
- Cardiac-brain coupling analysis
- Real-time monitoring capabilities
- Integration with EEG data
Biosignal Quality Assessment
Language: MATLAB/Python
Purpose: Automated quality control for physiological recordings
Features:
- Signal-to-noise ratio calculation
- Artifact quantification
- Channel quality assessment
- Automated reporting
- Database integration
VestibulabRecorder
Language: Python (PySide6)
Purpose: Posturography recording and galvanic vestibular stimulation platform
Features:
- Force-platform data recording
- Safe delivery of galvanic vestibular stimulation (GVS)
- GVS waveform generation
- Multiple recording and synchronization modes for different data streams
- Integration with VR software on a separate PC
vHITAnalyzer
Language: MATLAB
Purpose: Video head impulse test (vHIT) trial review and artifact detection
Features:
- Trial review interface
- Automatic artifact detection
- User-guided acceptance/rejection of vHIT trials
- Designed to support clinical validation of vHIT artifact detection
Cardio-Signal-Lab
Language: Python
Purpose: EKG analysis software for cardiac signal processing (greatly improved)
Features:
- R-peak detection and correction
- HRV parameter extraction
- Signal visualization and quality review
Technical Specifications
Programming Languages
- MATLAB: Advanced (10+ years)
- Python: Advanced (8+ years)
- R: Intermediate (5+ years)
- SQL: Intermediate
- TypeScript: Frontend and tooling development
- C++: Basic (for real-time applications)
Frameworks & Libraries
- Signal Processing: EEGLAB, FieldTrip, MNE-Python, SciPy
- Machine Learning: scikit-learn, TensorFlow, PyTorch
- GUI: Qt, PySide6, tkinter
- Statistics: SPM, R, statsmodels
- Visualization: matplotlib, plotly, ggplot2
- DevOps: Docker
AI Tooling
- Ollama: Local large language model inference
- Custom AI spec-flow orchestration: Multi-model planning, implementation, and review pipeline
- Custom AI memory tooling (bbmem): Persistent memory consolidation for AI-assisted development
Hardware Integration
- EEG Systems: BrainVision, Biosemi, Neuroscan
- ECG Systems: Biopac, PowerLab
- Stimulation: Arduino, National Instruments
- Computing: High-performance computing clusters
Code Availability
Many of these tools are available or will be made available through:
- GitHub repositories (upon publication)
- Laboratory websites
- Open science platforms
Requesting Access
For access to specific tools or collaboration on software development:
- Contact me directly through the contact page
- Specify your research needs and use case
- Collaboration and contribution welcome
Software development is an ongoing process. This page is updated as new tools are developed and existing ones are improved.