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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2410.07196 |
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| _version_ | 1866917799810564096 |
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| author | Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong |
| author_facet | Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong |
| contents | The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of 'EEG Parser', 'Correction', 'Batch Processing', and 'Large Language Model Boost'. Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at github.com/Baizhige/EEGUnity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07196 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model Qin, Chengxuan Yang, Rui You, Wenlong Chen, Zhige Zhu, Longsheng Huang, Mengjie Wang, Zidong Signal Processing Machine Learning The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of 'EEG Parser', 'Correction', 'Batch Processing', and 'Large Language Model Boost'. Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at github.com/Baizhige/EEGUnity. |
| title | EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2410.07196 |