SSVEP notebooks:
- SSVEP and machine learning by NeurotechX: https://neurotechx.github.io/eeg-notebooks/auto_examples/visual_ssvep/02r__ssvep_decoding.html
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PyMDE is a Python library for computing vector embeddings of items, such as images, biological cells, nodes in a network, or any other type of abstract object. The embeddings are designed to minimally distort relationships between pairs of items, while possibly satisfying some constraints. PyMDE is based on the monograph Minimum-Distortion Embedding, which introduced a simple but general framework for embedding.
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pyRiemann is a Python machine learning library based on scikit-learn API. It provides a high-level interface for classification and manipulation of multivariate signal through Riemannian Geometry of covariance matrices. pyRiemann aims at being a generic package for multivariate signal classification but has been designed around applications of biosignal (M/EEG, EMG, etc) classification.
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MNE: Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data: MEG, EEG, sEEG, ECoG, NIRS, and more.
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EMD: Empirical Mode Decomposition . Python tools for the extraction and analysis of non-linear and non-stationary oscillatory signals
- Time-series Generative Adversarial Networks: https://papers.nips.cc/paper/2019/file/c9efe5f26cd17ba6216bbe2a7d26d490-Paper.pdf
- https://github.com/jsyoon0823/TimeGAN