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joint-diagonalization

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lkorczowski / composite-joint-diagonalization-matlab

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Composite Approximate Joint Diagonalization (CAJD): blind source separation of EEG/ERP mixing linear (covariance, co-spectra) and bilinear (spatio-temporal) models. MATLAB code of Korczowski et al., EUSIPCO 2016.

erp signal-processing matlab neuroscience eeg unsupervised-learning bci brain-computer-interface tensor-decomposition event-related-potentials spatio-temporal p300 blind-source-separation denoising source-analysis noise-cancellation independent-component-analysis approximate-joint-diagonalization joint-diagonalization single-trial
  • Updated Nov 1, 2026
  • MATLAB

lkorczowski / composite-joint-diagonalization

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cajd: Composite Approximate Joint Diagonalization in Python — EEG/ERP source separation mixing linear (covariance) and bilinear (spatio-temporal) models. scikit-learn, MNE, MOABB and pyRiemann compatible.

python erp signal-processing scikit-learn neuroscience eeg mne bci brain-computer-interface tensor-decomposition event-related-potentials p300 blind-source-separation moabb pyriemann joint-diagonalization
  • Updated Nov 3, 2026
  • Python

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