Metadata-Version: 2.1
Name: telluride-decoding
Version: 2.1.0
Summary: Telluride Decoding Toolbox
Home-page: https://github.com/google/telluride_decoding
Author: Malcolm Slaney
Author-email: telluride-decoding-maintainers@googlegroups.com
License: UNKNOWN
Description: # The telluride_decoding Library
        
        (This is not an official Google product!)
        
        This repository contains Python/Tensorflow code to decode perceptual signals
        from brain data.  The perceptual signals we are using are generally audio
        features.  And the brain data is one of several types of signals, such as EEG,
        MEG and ECoG.
        
        The Telluride Decoding Toolbox is geared towards large-scale decoding
        experiments. You can
        run small experiments in a colab. But this package keeps all the data in
        files so the data doesn't have to fit into memory, and so it is easier to run
        tens or hundreds of tests in parallel.
        
        ## License
        
        This code uses the Apache License 2.0. See the LICENSE file for details.
        
        
        ## Purpose
        This code builds and trains models that connect perceptual signals, primarily
        audio, to brain signals, which can be EEG, MEG or ECOG. One common use for this
        type of algorithm is to decode auditory attention, as shown in the figure below.
        
        ![Auditory attention decoding](https://raw.githubusercontent.com/google/telluride_decoding/master/doc/AuditoryAttentionDecoding.jpg)
        
        In attention decoding, we wish to know which of two (or more) signals a user is
        attending. One signal (the upward pointing blue arrow) indicates that the signal
        is being processed by the entire brain and is "exciting" all areas.  Another
        signal is heard by the auditory system, but is not attended, and doesn't
        recruit as much of the brain. The two signals are processed differently, and
        produce different brain signals.
        
        This software helps to decide which signal the user is attending, by building
        a model that uses the EEG signal, for example, to predict the intensity of the
        attended audio signal.
        
        ## Documentation
        Documentation to follow (but there are extensive comments in the code.) The
        three primary parts of this code are:
        
        * ingest: Use to read various kinds of file formats and transform the data into
        TFRecords for use by the decoding program
        * decoding: Used to build, train and test models that connect audio and brain
        signals.
        * infer: Use to run a model on real or test data.  This model can run in real
        time.
        
        ## To install
        To install this software you can execute this command, which will automatically
        install the necessary prerequisites:
        
        ```
        pip install telluride-decoding
        ```
        
        ## Using this code
        This library is written in Python (2 or 3) and uses Tensorflow. The
        decoding code can be run as a standalone program, or as a library, in for
        instance a [Colab](https://colab.research.google.com).
        
        Documentation on the standalone decoding program can be found
        [here](https://github.com/google/telluride_decoding/blob/master/doc/decoding.md). This version is useful in distributed environments,
        where one might want to do a parameter search.  Each run of the program
        tests one set of parameters.
        
        One can also use the program as a library in a Colab. Colab allows one to
        interactively ingest data, build models, and test their performance. Here are
        some examples:
        
        * Telluride4: Ingest and test linear regression.
        [colab](https://colab.sandbox.google.com/drive/1VDbHlClIMBNvlneN8gZ8cmxsFWxi4u7T)
        
        
        The code uses CPU or GPUs, as available. TPU support is forthcoming.
        
        ## References
        
        James O'Sullivan, AJ Power, Nima Mesgarani, S. Rajaram, John Foxe,
        Barbara Shinn-Cunningham, Malcolm Slaney, Shihab Shamma, Edward Lalor.
        Attentional Selection in a Cocktail Party Environment Can Be Decoded from
        Single-Trial EEG.
        _Cereb Cortex_. 2015 Jul;25(7):1697-706.
        
        
        Daniel D.E. Wong,  Søren A. Fuglsang,  Jens Hjortkjær, Enea Ceolini,
        Malcolm Slaney, Alain de Cheveigné.
        A Comparison of Temporal Response Function Estimation Methods for
        Auditory Attention Decoding.
        _Frontiers in Neuroscience_. doi: 10.3389/fnins.2018.00531.
        
        This package contains Python and Tensorflow code for the Deep Canonical
        Correlation Analysis algorithm. (An earlier MATLAB implementation can be
        found at https://ttic.uchicago.edu/~wwang5/dccae.html)
        
        Wang, Weiran and Arora, Raman and Livescu, Karen and Bilmes, Jeff.
        On deep multi-view representation learning,
        _International Conference on Machine Learning_, pp. 1083-1092,
        2015.
        
        Andrew, Galen and Arora, Raman and Bilmes, Jeff and Livescu, Karen.
        Deep canonical correlation analysis.
        _International conference on machine learning_, pp. 1247-1255,
        2013. https://ttic.uchicago.edu/~klivescu/papers/andrew_icml2013.pdf
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
