EEGraph: An open-source Python library for modeling electroencephalograms using graphs
•Open-source Python library for graph-based EEG modeling using connectivity measures.•Includes several connectivity measures, related to time and frequency domains.•Allows flexibility to define specific parameters related to the analysis of EEGs.•Output is provided in two formats: data structures an...
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| Published in: | Neurocomputing (Amsterdam) Vol. 519; pp. 127 - 134 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
28.01.2023
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| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
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| Summary: | •Open-source Python library for graph-based EEG modeling using connectivity measures.•Includes several connectivity measures, related to time and frequency domains.•Allows flexibility to define specific parameters related to the analysis of EEGs.•Output is provided in two formats: data structures and graph visual representation.•Promotes the use of EEGs as a clinical test for the study of connectivity patterns.
Connectivity studies make it possible to identify alterations in brain connections and to associate these pathologies with different neurological disorders. However, a clinical test is necessary to obtain information about the state of the brain. Electroencephalograms (EEGs) provide this information in addition to being tests with other benefits for the patient (non-invasive, low-cost, high reproducibility). Graph theory can be used to represent both the anatomical and functional connections of the brain by means of connectivity measures. The procedure of transforming an EEG into a graph can be slightly tedious for researchers, especially when implementing different connectivity measures.
The open-source Python library EEGraph automatically performs the modeling of an EEG through a graph, providing its matrix and visual representation. It recognizes various EEG input formats, identifying the number of electrodes and the location of each electrode in the brain. Moreover, it allows the user to choose from 12 connectivity measures to produce the graph from the EEG, with great flexibility to define specific parameters to adapt them to each study, including EEG time-windows segmentation and separation in frequency bands.
The EEGraph library is developed as a tool, for researchers and clinical specialists in the field of neuroscience, that provides direct information on the connectivity of the brain from electroencephalography signals. Its documentation and source code are available athttps://github.com/ufvceiec/EEGRAPH. It can be installed from the Python Package Index using pip install EEGRAPH.
The EEGraph library was built aiming to facilitate the development of connectivity studies based on the modeling of electroencephalography tests through graphs. It includes a wide range of connectivity measures, which, together with the multiple output options, make EEGraph an easy to use and powerful tool with direct applications in both the clinical and neuroscience research fields. |
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| ISSN: | 0925-2312 1872-8286 |
| DOI: | 10.1016/j.neucom.2022.11.050 |