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cse481_spectrogram_example

Example code for creating spectrograms from EEG data

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

decision-trees-for-knowledge-discovery

Understand and modelize the structure behind your data with Decision Trees

deeplabcut

Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans

deeptime

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

denoiseeeg-gan

Repository for Denoising EEG using GANs

dsw2018-tutorials

Tutorials for GDSO at Berkeley Data Science Workshop

eegparam

EEG data analyses for the Parameterizing Neural Power Spectra paper.

ephys-class

Course materials for COGS 280

eraasr

ERAASR: An algorithm for removing electrical stimulation artifacts from multielectrode array recordings

facemap

matlab/python GUI for unsupervised video analysis of rodent behavior (capable of processing multiple camera views)

fieldtrip

The MATLAB toolbox for MEG, EEG and iEEG analysis

fissa

A Python toolbox for Fast Image Signal Separation Analysis, designed for Calcium Imaging data.

functional_parcellation

galaxybrain

Decomposition of neuronal correlation structure in space and time

ggnb-python-course

GGNB Short Method Course "Spike-train analysis with Python" in the Gollisch Lab

glmspiketraintutorial_python

gp_algorithm

Implementation of the Grassberger-Procaccia algorithm to estimate the Correlation Dimension of a set of points

hackathons

Jupyter notebooks from our weekly (or so) hackathons

hmm-mne

Hidden Markov Modelling of M/EEG data.

hs2

Software for high density electrophysiology

hydra-neural-activity-analysis

Single-cell neural activity processing toolkit for calcium imaging recordings of deformable organisms

icsd

Python-implementation of the inverse current source density (iCSD) methods from http://software.incf.org/software/csdplotter

indonesian-rainfall-pattern-classification-using-time-series-k-means

Using time series k-means to cluster three major Indonesian rainfall patterns. The distance computation in the k-means is Dynamic Time Warping (DTW), which is commonly used for pattern matching and temporal/sequential data clustering.

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

kcsd-python

Kernel Current Source Density

lead-lag

Estimation of the lead-lag parameter from non-synchronous data.

lehrprobeuol

Lehrprobe: Spike train analysis using Python an der Carl von Ossietzky Universität Oldenburg

machine-learning

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

machine-learning-for-time-series-with-python

Machine Learning for Time-Series with Python.Published by Packt