A comprehensive workshop series on EEG connectivity analysis for hyperscanning — from signal processing fundamentals to advanced inter-brain connectivity metrics, published as a Jupyter Book.
Designed for graduate students and researchers in neuroscience, psychology, and biomedical engineering. Assumes basic familiarity with EEG and Python. No prior signal processing expertise required.
By the end of this series, you will be able to:
- Decompose EEG signals into frequency components and extract band power
- Apply filtering, compute the Hilbert transform, and work with instantaneous phase
- Understand why volume conduction is a fundamental challenge in EEG connectivity
- Implement from scratch: PLV, PLI, wPLI, coherence, imaginary coherence, envelope correlation, mutual information, and transfer entropy
- Apply these metrics in a hyperscanning (dual-brain) context
- Validate results with the HyPyP library
| Block | Topic | Notebooks |
|---|---|---|
| A | Signal Fundamentals | A01 Signals & Sampling · A02 Frequency Domain · A03 Power Spectrum · A04 Filter Fundamentals · A05 Applied Filtering |
| B | Phase & Amplitude | B01 Hilbert Transform · B02 Circular Statistics · B03 Phase Relationships · B04 Amplitude Envelope · B05 Envelope Correlation · B06 STFT & Spectrograms · B07 Wavelet Analysis |
| C | Connectivity Concepts | C01 Volume Conduction · C02 Matrix Computation · C03 Matrix Visualization · C04 Statistical Significance |
| D | Information Theory | D01 Entropy · D02 Mutual Information · D03 Transfer Entropy |
| E | Hyperscanning Context | E01 Graph Theory · E02 Hyperscanning Concepts · E03 Hyperscanning Data · E04 Hyperscanning Analysis |
| Block | Topic | Notebooks |
|---|---|---|
| F | Coherence-Based | F01 Coherence Theory · F02 Coherence Practice · F03 Imaginary Coherence |
| G | Phase-Based | G01 PLV · G02 PLI · G03 wPLI |
| H | Amplitude-Based | H01 Envelope Correlation · H02 Power Correlation |
| I | Information-Theoretic | I01 MI Connectivity · I02 Transfer Entropy Connectivity |
The book is published at ppsp-team.github.io/ConnectivityMetricsTutorials — no installation required.
git clone https://github.com/ppsp-team/ConnectivityMetricsTutorials.git
cd ConnectivityMetricsTutorials
uv sync
uv run jupyter lab book/Prerequisites: Python 3.12+, uv
To build the full HTML book locally:
uv run jupyter-book build
open _build/html/index.htmlbook/ # Jupyter Book content (published on GitHub Pages)
├── 01_foundations/ # Blocks A–E: signal processing foundations
└── 02_connectivity_metrics/ # Blocks F–I: connectivity metrics
myst.yml # Jupyter Book configuration (mystmd)
src/ # Reusable Python modules (imported by all notebooks)
archive/ # Original notebook drafts (reference only)
docs/ # Style guides, review reports, JB template
data/ # Raw and processed EEG data
| Document | Description |
|---|---|
| docs/CONTEXT.md | Project context, audience, and pedagogical approach |
| docs/STYLE_GUIDE.md | Visual and code style standards |
| docs/JB_STYLE_GUIDE.md | MyST/Jupyter Book formatting guide |
| docs/JB_NOTEBOOK_TEMPLATE.ipynb | Template for new notebooks |
| docs/reviews/ | Sprint review reports |
MIT — see LICENSE.