This repository, "Neural Signal Processing", is dedicated to signal processing techniques applied to neural data. The primary focus will be on analyzing electroencephalography (EEG) and other bio-signals. The repository will leverage various Python packages specialized in different aspects of signal processing.
The following Python packages will be predominantly utilized for signal processing tasks:
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MNE-Python: A comprehensive library for EEG data processing, including reading, preprocessing, visualization, and analysis.
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NeuroKit2: This library provides tools for EEG and bio-signal analysis, offering features for preprocessing, analysis, and visualization.
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YASA: YASA is used for spectral analysis and sleep staging of EEG data. It provides functions for time-frequency analysis and sleep stage classification.
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Frites: Frites is a tool for connectivity analysis and statistical testing, particularly useful for investigating functional brain networks.
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Entropy: A library for entropy-based time series analysis, offering functions for computing various entropy measures.
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Tensorpac: Tensorpac is employed for phase-amplitude coupling analysis, a technique used to study the relationship between different frequency components of neural oscillations.
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Visbrain: Visbrain provides tools for 3D visualization of neural data and brain connectivity, enabling interactive exploration of brain activity patterns.
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Neurodsp: Neurodsp is used for digital time series data analysis, with a focus on oscillatory signals. It offers functions for filtering, oscillatory component detection, and parameter estimation.
These packages collectively offer a comprehensive suite of tools for analyzing neural signals, covering a wide range of preprocessing, analysis, and visualization tasks.
To utilize the functionalities provided by this repository, ensure that you have the necessary Python packages installed. You can install them using pip:
pip install mne neurokit2 yasa frites entropy tensorpac visbrain neurodspPlease refer to the documentation of each package for detailed information on their usage and functionalities.
Contributions to this repository are welcome! If you have any suggestions, bug fixes, or feature implementations, feel free to open an issue or submit a pull request.