Precision Neural Engineering Lab
ePOCHE
Visual EEG-to-model pipelineePOCHE is a GUI-driven time-series classification platform that takes you from raw EEG to trained models—with feature engineering, model management, and automated evaluation—all through a visual interface.
Built for researchers who need to iterate on classification approaches without rebuilding infrastructure every time.
ePOCHE is designed as a domain-specific IDE for biosignal classification: visual pipelines, systematic comparison, and reproducibility by default.
ePOCHE — Visual EEG-to-model classification pipeline
Platform Architecture
ML Pipeline Flow
What Problem It Solves
Most EEG classification workflows assume:
- You'll write throwaway scripts for each experiment
- You'll reinvent preprocessing, feature extraction, and evaluation every time
- Reproducibility is your problem—buried in ad-hoc code
- You have deep ML engineering knowledge (or will acquire it)
This creates fragile pipelines, inconsistent results, and weeks lost to rebuilding infrastructure that should exist once and work everywhere.
ePOCHE replaces that chaos with a visual pipeline you can configure, run, and reproduce.
What Makes ePOCHE Different
Visual Configuration
- GUI-driven pipeline — configure the entire workflow visually
- No code required — researchers can iterate without engineering support
- Reproducible by design — every run captures its exact configuration
Signal Processing
- Multi-format ingestion — EDF, BDF, CSV supported out of the box
- Preprocessing pipeline — filtering, resampling, epoch extraction
- Signal quality metrics — automated artifact detection before you waste compute
Feature Engineering
- Time-domain features — statistical measures across epochs
- Frequency-domain features — FFT, power spectral density
- Time-frequency features — wavelet transforms for non-stationary signals
- Configurable extraction — select exactly what matters for your task
Model Management
- Unified API — scikit-learn and TensorFlow models through the same interface
- Automated cross-validation — configurable strategies without boilerplate
- Hyperparameter optimization — grid and random search built in
- Comparative visualization — evaluate model performance side by side
Export & Reproducibility
- Trained model export — save and reload models
- Feature importance reports — understand what drives predictions
- Reproducibility logs — every parameter captured for publication
Designed for How Researchers Actually Work
ePOCHE is not just a script collection—it is a thinking environment for biosignal classification:
- You explore data before committing to a model
- You iterate on features without rewriting pipelines
- You compare approaches systematically, not ad-hoc
- You export results that reviewers can reproduce
Why This Exists
ePOCHE grew out of watching researchers rebuild the same infrastructure for every EEG study—loading data, extracting features, training models, generating figures. The exact pipeline was always buried in scripts that worked once and broke forever.
The abstraction layer means the same tool works for EMG, ECG, GSR—any biosignal classification. Swap data sources and model types without touching underlying code.
Platform lesson: Researchers don't want to rebuild the same ML pipeline infrastructure for every study—they want to iterate on classification approaches. The same way developers don't want to rebuild CI/CD for every project—they want consistent, reproducible workflows. ePOCHE is a control plane for biosignal classification.
Impact
- Reduced pipeline setup from days to hours
- Enabled systematic comparison of classification approaches
- Made results reproducible by default
- Accessible to researchers without ML engineering background
Who It's For
- Cognitive neuroscience researchers
- BCI developers prototyping classification
- Graduate students learning ML for biosignals
- Anyone who needs reproducible EEG/EMG/ECG analysis