What are the most well-known and tested Python libraries for reading EEG signals, and how do they compare in terms of adoption, ecosystem, features, and maintenance? Let’s find out.

Neurophysiological signals are electrical and magnetic signals generated by the human brain and nervous system. These signals are produced by the activity of neurons and are measured using various methods such as electroencephalography (EEG), magnetoencephalography (MEG), and electrocardiography (ECG). They come in the form of time-series data, where each point in the series represents the magnitude of the signal at a specific point in time. The analysis of these signals can provide insights into brain function and can be used in various applications, such as neuroscience research, clinical diagnosis, and human-computer interaction.

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There are several well-known and tested Python libraries for reading EEG signals. Here are some of the most popular ones:

  • MNE-Python: This is a comprehensive library for analyzing EEG, MEG, and other neurophysiological data. It can read data from various file formats and has a wide range of built-in tools for preprocessing, analysis, and visualization.
  • PyEEG: This library is focused on basic EEG signal processing and analysis, including time-domain and frequency-domain analysis, entropy-based measures, and other related functions.
  • NeuroKit: This library is focused on physiological signal processing and analysis, including EEG. It has functions for reading data from various file formats, preprocessing, and analysis, as well as visualization tools.
  • Brainflow: In contrast with the libraries above, brainflow focuses primiarily on obtaining EEG, EMG, ECG, and other biosignals from various hardware devices. It provides a unified interface and can read data in real-time or from files. It also has built-in tools for preprocessing, feature extraction, and analysis.

These libraries can greatly simplify the process of working with EEG data in Python, and provide a wide range of tools and functionality for signal processing, analysis, and visualization.

Comparison of libraries

Adoption and User Base

In terms of adoption and user base, MNE-Python has the largest user base and is one of the most widely used EEG signal processing libraries in the Python ecosystem. PyEEG and EEG-Notebooks have a smaller user base but are still popular among researchers and developers. NeuroKit and Brainflow are relatively newer libraries and have a smaller user base.

Ecosystem and Features

MNE-Python has a large ecosystem with many built-in tools and functions for preprocessing, analysis, and visualization. PyEEG, EEG-Notebooks, NeuroKit, and Brainflow are more focused on specific tasks, but still provide a good set of tools for EEG signal processing and analysis. Additionally, all of these libraries have the ability to read EEG data from various file formats.


MNE-Python has a large and active development team, and is well-maintained with frequent updates and bug fixes. PyEEG, EEG-Notebooks, NeuroKit, and Brainflow also have active development teams and are regularly updated.

Overall, MNE-Python is the most widely adopted and feature-rich library for EEG signal processing and analysis, but the other libraries also provide useful tools and functionality for working with EEG data.


  • https://mne.tools/stable/index.html
  • https://github.com/neuropsychology/NeuroKit
  • https://brainflow.org/
  • https://github.com/brainflow-dev/brainflow
  • https://github.com/forrestbao/pyeeg