Metadata-Version: 2.1
Name: brainsurf
Version: 7.0.0
Summary: EEG Signal Processing Library
Home-page: https://github.com/preethihiremath/brainsurf
Author: preethivhiremath
Author-email: preethivhiremath.vh@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Description-Content-Type: text/markdown
License-File: LICENSE

# Description
BrainSurf is a Python library for processing and analyzing EEG (electroencephalography) signals. It provides a collection of tools and methods for reading, preprocessing, analyzing, and visualizing EEG data. The library is built using the NumPy, SciPy, and Matplotlib packages and is designed to be easily extensible for custom analysis and visualization needs

# Installation
BrainSurf can be installed using pip, a Python package manager. To install the latest stable version of the library, run the following command :

`pip install brainsurf`

# Github
Alternatively, you can clone the repository from GitHub and install it from source:
`git clone https://github.com/preethihiremath/brainsurf`
`cd esp`
`pip install -r requirements.txt`
`python setup.py install`


# Usage
```python
import brainsurf.io.mff as load_input
import brainsurf.utils.data as util
import brainsurf.utils.performance as performance
import brainsurf.preprocessing.filtering as filter
import brainsurf.preprocessing.artifact_removal as artifact
import brainsurf.preprocessing.epoching as epoching

#load EEG data from file
suriya_baseline = load_input.convert_mff_to_eegdata("C:/Users/Preethi V Hiremath/Downloads/Meditators/Suriya/BS.mff")


values = np.asarray(suriya_baseline['sec'], dtype=object)
sampling_freq = util.estimate_sampling_frequency(values)


pre_preprocessed_data = preprocess_eeg_data(suriya_baseline,sampling_freq)

print(performance.calculate_memory_efficiency())
performance.monitor_resource_usage()

