1112003001

1112003001's Stars

• PyWavelets/pywt

  PyWavelets - Wavelet Transforms in Python

  Language:Python2k62333470

• vlawhern/arl-eegmodels

  This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

  Language:Python1.2k4142286

• pyRiemann/pyRiemann

  Machine learning for multivariate data through the Riemannian geometry of positive definite matrices in Python

  Language:Python62531105164

• moble/quaternion

  Add built-in support for quaternions to numpy

  Language:Python6092317285

• beringresearch/ivis

  Dimensionality reduction in very large datasets using Siamese Networks

  Language:Python330136043

• aliasvishnu/EEGNet

  [Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf

  Language:Jupyter Notebook28010766

• forrestbao/pyeeg

  Python + EEG/MEG = PyEEG

  Language:Python247121986

• ncclabsustech/EEGdenoiseNet

  EEGdenoiseNet, a benchmark dataset, that is suited for training and testing deep learning-based EEG denoising models, as well as for comparing the performance across different models.

  Language:Jupyter Notebook1603943

• mne-tools/mne-features

  MNE-Features software for extracting features from multivariate time series

  Language:Python140134732

• hli1221/imagefusion_Infrared_visible_latlrr

  infrared anf visible image fusion using latent low-rank representation

  Language:MATLAB705334

• ncclabsustech/DeepSeparator

  Deep learning model for EEG artifact removal

  Language:Python561514

• zhangzg78/eegbook

  Language:MATLAB542116

• sccn/ICLabel

  Automatic EEG IC classification plugin for EEGLAB

  Language:MATLAB5372819

• axiqia/Anomaly-Detection-in-Hyperspectral-Images-Based-on-Low-Rank-and-Sparse-Representation

  Language:MATLAB382316

• FWen/ncreg

  A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

  Language:MATLAB383018

• robintibor/auto-eeg-diagnosis-example

  Language:Python353217

• hli1221/imagefusion_noisy_lrr

  multi-focus image fusion using low-rank representation

  Language:MATLAB284017

• polwork/Hyperspectral-Image-Denoising-via-Sparse-Representation-and-Low-Rank-Constraint

  Zhao Y Q, Yang J. Hyperspectral image denoising via sparse representation and low-rank constraint[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 296-308.

  Language:MATLAB28108

• AlexWufan/Robust-Subspace-Segmentation-By-Low-Rank-Representation

  Language:Matlab211012

• sagihaider/Common-Spatial-Pattern-Algorithm

  Common spatial pattern (CSP) is a mathematical procedure used in signal processing for separating a multivariate signal into additive subcomponents which have maximum differences in variance between two windows. This algorithm is mainly used in motor imagery based BCI for processing EEG data.

  Language:MATLAB205010

• TNTLFreiburg/brainfeatures

  A toolbox to decode raw time-domain EEG using features.

  Language:Python17254

• ncclabsustech/Single-Channel-EEG-Denoise

  Language:Jupyter Notebook14001

• lucapton/ICLabel-Dataset

  Dataset for training EEG IC classifiers.

  Language:Python12131

• nhatpd/iADMM

  iADMM for a low-rank representation optimization problem

  Language:MATLAB12305

• Ayantika22/Linear-discriminant-Analysis-LDA-for-Wine-Dataset

  Linear discriminant Analysis(LDA) for Wine Dataset of Machine Learning

  Language:Jupyter Notebook8105

• lucapton/ICLabel-Train

  Files for training the ICLabel classifier in tensorflow

  Language:Python5302

• XiaTaopsycho/Hulab_EEG_preprocessing_tutorial

  I prepared a MNE-BIDS and MNE python tutorial for Hulab

  Language:Jupyter Notebook40

• akrlowicz/eeg-eye-state-recognition

  Using machine learning models topredict eye state of the subject based on preprocessed EEG signal. Includes preprocessing the data, feature extraction and selection, dimensionality reduction and visualization.

  Language:Jupyter Notebook1100

• gauravSingh30/DeepLearning-Questions

  深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06

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• gauravSingh30/EEG-MotorActivity-Classification

  EEG 4-class Motor Classification with Deep Learning Architectures and different preprocessing techniques

  Language:Jupyter Notebook1