Mathematical Derivation: SVD Whitening Implementation
Published:
Using Singular Value Decomposition (SVD) for robust whitening in eegwhiten.
Blog Posts
Mathematical Derivation: ZCA Whitening Implementation
Published:
Understanding Zero-phase Component Analysis (ZCA) whitening in eegwhiten.
Mathematical Derivation: PCA Whitening Implementation
Published:
A deep dive into the PCA whitening algorithm implemented in the eegwhiten package.
Train/Test-Safe EEG Feature Extraction
Published:
A plain guide to fitting EEG feature extractors without leaking test information.
Mathematical Derivation: Cholesky Whitening Implementation
Published:
Efficient whitening using Cholesky decomposition in the eegwhiten package.
CSP and FBCSP for Motor-Imagery EEG
Published:
A direct walkthrough of how CSP and filter-bank CSP turn motor-imagery EEG trials into classifier features.
Covariance Features and Tangent-Space Mapping
Published:
A simple explanation of why EEG covariance matrices are useful features and how tangent-space mapping makes them usable by standard classifiers.
ICA Objective Functions in One Place: Kurtosis, Negentropy, MI, and MLE
Published:
A compact map of the main ICA objective functions and their algorithmic implications.
What CORAL, SA, TCA, and OT Actually Align
Published:
A plain comparison of four domain-adaptation methods through the object each method tries to align.
From Matrix ICA to Tensor ICA: Architectures and Decomposition Choices
Published:
A concise bridge from classical ICA to multilinear/tensor ICA design choices.
Multi-Source Domain Adaptation for EEG Sessions
Published:
A practical view of how multiple source sessions can help or hurt cross-session EEG transfer.
Practical Guide: Distribution Distance Metrics for EEG Domain Adaptation
Published:
A practical note on Wasserstein, MMD, and Energy Distance for quantifying session-to-session shift in EEG pipelines.
When Domain Adaptation Helps and When It Fails
Published:
A direct guide to reading when EEG domain adaptation is useful and when it causes negative transfer.
Research Note: Markov-Switching State-Space Models for Neural Decoding
Published:
A working note on using regime-switching linear state-space models for non-invasive brain-signal decoding.