Metadata-Version: 2.1
Name: PyEEGLab
Version: 0.9.3
Summary: Analyze and manipulate EEG data using PyEEGLab
Home-page: https://github.com/AlessioZanga/PyEEGLab
Author: Alessio Zanga
Author-email: alessio.zanga@outlook.it
License: GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007
Description: # PyEEGLab
        
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        Analyze and manipulate EEG data using PyEEGLab.
        
        ## Introduction
        
        PyEEGLab is a python package developed to define pipeline for EEG preprocessing for a wide range of machine learning tasks. It supports set of datasets out-of-the-box and allow you to adapt your preferred one.
        
        ## How it Works
        
        Here is a simple quickstart:
        
            from pyeeglab import *
            dataset = TUHEEGAbnormalDataset()
            pipeline = Pipeline([
                CommonChannelSet(),
                LowestFrequency(),
                ToDataframe(),
                MinMaxCentralizedNormalization(),
                DynamicWindow(8),
                ToNumpy()
            ])
            dataset = dataset.set_pipeline(preprocessing).load()
            data, labels = dataset['data'], dataset['labels']
        
        In this example, for each sample in the dataset, a common set of electrodes is selected, then downsampled to the lowest frequency and normalized using a min-max centralized approach. Each sample is then splitted in eight windows or frames.
        
        This approach is quite usefull for tasks like artifact classification or seizure detection.
        
        ## How to Install
        
        PyEEGLab is distributed using the pip repository:
        
            pip install PyEEGLab
        
        If you use Python 3.6, the dataclasses package must be installed as backport of Python 3.7 dataclasses:
        
            pip install dataclasses
        
        If you need a bleeding edge version, you can install it directly from GitHub:
        
            pip install git+https://github.com/AlessioZanga/PyEEGLab@develop
        
        ## Out-Of-The-Box Supported Datasets
        
        The following datasets will work upon downloading:
        
        * [Temple University Abnormal EEG Dataset](https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml)
        * [Temple University Artifact EEG Dataset](https://www.isip.piconepress.com/projects/tuh_eeg/html/downloads.shtml)
        * [EEG Motor Movement/Imagery Dataset](https://physionet.org/content/eegmmidb/1.0.0/)
        
        ## How to Get a Dataset
        
        > **WARNING (1)**: Retriving the TUH EEG Abnormal dataset require at least 65GB of free disk space.
        
        > **WARNING (2)**: Retriving the TUH EEG Abnormal dataset require valid credentials, you can get your own at https://www.isip.piconepress.com/projects/tuh_eeg/html/request_access.php.
        
        In the root directory of this project there is a Makefile, by typing:
        
            make tuh_eeg_abnormal
        
        you will trigger the dataset download.
        
        ## Documentation
        
        > **WIP**: Documentation is currently Work-In-Progress, if you need additional info, please, contact me directly.
        
        You can find the documentation at https://pyeeglab.readthedocs.io
        
        ## Credits
        
        If you use this code in your project use the citation below:
        
            @misc{Zanga2019PyEEGLab,
                title={PyEEGLab: A simple tool for EEG manipulation},
                author={Alessio Zanga},
                year={2019},
                doi={10.5281/zenodo.3874461},
                url={https://dx.doi.org/10.5281/zenodo.3874461},
                howpublished={\url{https://github.com/AlessioZanga/PyEEGLab}},
            }
        
        ## Related publications
        
        - "An Attention-based Architecture for EEG Classification" - https://doi.org/10.5220/0008953502140219
        
Platform: Environment :: Console
Platform: Operating System :: POSIX :: Linux
Platform: Operating System :: Microsoft :: Windows :: Windows 10
Platform: Operating System :: MacOS
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
