EEG resources

[DominiqueMakowski]

Starting to gather some EEG resources, ideas and cool other toolboxes for future reference.

Essentially, the way I see it, we could in the future thing about the usefulness of wrappers for MNE (similarly to as we have wrappers for scipy.signal) and also some complementary features (for instance, single-trials ERP, complexity and microstates).

Simulation

• visbrain

Preprocessing

• NeuroPycon - full paper
• pyprep
• YASA
• MEEGkit
• DeepEEG
• Harvard Automated Processing Pipeline for Electroencephalography (HAPPE)
• SGEYESUB
• LAP reference-free / CSD referencing
• MARA (Multiple Artifact Rejection Algorithm for ICA labelling in Matlab)
• SuBAR: surrogate-based artifact removal
• CCA-based muscle-removal

Microstates

See https://github.com/neuropsychology/NeuroKit/issues/332

ERP

• [ ] GAM-based analysis (see nolds for matlab)
• [ ] Regression-based estimation of ERP (rERP; see mne)

Single-trial ERP:

• [ ] Ahmadi, 2013
• [ ] Huang, 2015
• [ ] Mowla, 2016
• [ ] Yu, 2017

Complexity

• [x] Finalize #40
• [ ] MNE-compatible interface to apply complexity analysis on MNE objects

See pyeeg; AntroPy.

Connectivity

Measures of interaction between sensors (see Lebedev, 2017)

• Causality, coherence, transfer function

Spatiotemporal dynamics

Forward and backward waves (FW and BW).

• [Turning the Stimulus On and Off Dynamically Changes the Direction of Alpha Traveling Waves; Pang et al. 2020]()https://www.biorxiv.org/content/10.1101/2020.04.15.041756v2

Others

• Riemannian Geometry: see pyRiemann, a package for covariance matrices manipulation and classification through Riemannian geometry used in the classification of multivariate biosignals, like EEG, MEG or EMG.
• MS-ROM/IFAST (Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time): see Grossi (2019)

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