The MNE software is a comprehensive tool for processing magnetoencephalography (MEG) and electroencephalography (EEG) data. It provides a range of analysis tools and workflows, including preprocessing, source estimation, time–frequency analysis, statistical analysis, and methods for estimating functional connectivity between brain regions. The software is a collaborative effort by multiple institutions aiming to implement and share best practices for M/EEG analysis, enhancing the reproducibility of research. MNE includes three core subpackages: MNE-C, MNE-Matlab, and MNE-Python, all using the same FIF file format and compatible analysis steps. MNE-Python is the most recent addition, offering features such as time–frequency analysis, non-parametric statistics, and connectivity estimation. The software supports both sensor and source space analyses, with methods for noise reduction, artifact rejection, and statistical testing. MNE also provides functions for functional connectivity estimation, including bivariate spectral measures like coherence and phase-locking value. The software is designed to handle complex, computationally demanding tasks in M/EEG data processing, with efficient processing pipelines and parallel computing capabilities. MNE supports various inverse modeling approaches, including minimum norm estimates, distributed solutions, and beamformers, each with its own assumptions and strengths. The software also includes tools for forward modeling, including boundary element methods (BEM) and sensor-specific configurations. MNE is used for preprocessing, forward modeling, inverse modeling, and statistical analysis, with a focus on accurate source localization and functional connectivity estimation. The software is widely used in neuroscientific research for its comprehensive features and ability to support a wide range of analysis tasks.The MNE software is a comprehensive tool for processing magnetoencephalography (MEG) and electroencephalography (EEG) data. It provides a range of analysis tools and workflows, including preprocessing, source estimation, time–frequency analysis, statistical analysis, and methods for estimating functional connectivity between brain regions. The software is a collaborative effort by multiple institutions aiming to implement and share best practices for M/EEG analysis, enhancing the reproducibility of research. MNE includes three core subpackages: MNE-C, MNE-Matlab, and MNE-Python, all using the same FIF file format and compatible analysis steps. MNE-Python is the most recent addition, offering features such as time–frequency analysis, non-parametric statistics, and connectivity estimation. The software supports both sensor and source space analyses, with methods for noise reduction, artifact rejection, and statistical testing. MNE also provides functions for functional connectivity estimation, including bivariate spectral measures like coherence and phase-locking value. The software is designed to handle complex, computationally demanding tasks in M/EEG data processing, with efficient processing pipelines and parallel computing capabilities. MNE supports various inverse modeling approaches, including minimum norm estimates, distributed solutions, and beamformers, each with its own assumptions and strengths. The software also includes tools for forward modeling, including boundary element methods (BEM) and sensor-specific configurations. MNE is used for preprocessing, forward modeling, inverse modeling, and statistical analysis, with a focus on accurate source localization and functional connectivity estimation. The software is widely used in neuroscientific research for its comprehensive features and ability to support a wide range of analysis tasks.