Getting Started

EEGProc is a fully vectorized library designed for preprocessing and extracting features from EEG(Electroencephalogram) data. This library is optimized for performance and ease of use, making it suitable for researchers and developers working in the field of neuroscience, biomedical engineering, and machine learning.

Installation

Install from PyPI:

pip install eegproc

or, for the latest development version:

pip install git+https://github.com/VitorInserra/EEGProc.git

Dependencies

EEGProc relies on:

  • NumPy, Pandas, SciPy – numerical processing

  • PyWavelets – wavelet features

  • PyEMD – empirical mode decomposition

  • Matplotlib – plotting utilities

Quick Start

  1. Import and load your EEG data:

import pandas as pd
from eegproc import bandpass_filter, FREQUENCY_BANDS

df = pd.read_csv("my_eeg_data.csv")
fs = 128  # Hz

  1. Filter and extract features:

clean = bandpass_filter(df, fs, bands=FREQUENCY_BANDS)
from eegproc import shannons_entropy, hjorth_params

entropy_df = shannons_entropy(clean, fs)
hjorth_df  = hjorth_params(clean, fs)

  1. Visualize results:

from eegproc.plotting import plot_per_channel
plot_per_channel(entropy_df, title="Shannon Entropy per Channel")

Documentation Structure