SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916162759032832 |
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| author | Del Pup, Federico Zanola, Andrea Tshimanga, Louis Fabrice Mazzon, Paolo Emilio Atzori, Manfredo |
| author_facet | Del Pup, Federico Zanola, Andrea Tshimanga, Louis Fabrice Mazzon, Paolo Emilio Atzori, Manfredo |
| contents | SelfEEG is an open-source Python library developed to assist researchers in conducting Self-Supervised Learning (SSL) experiments on electroencephalography (EEG) data. Its primary objective is to offer a user-friendly but highly customizable environment, enabling users to efficiently design and execute self-supervised learning tasks on EEG data.
SelfEEG covers all the stages of a typical SSL pipeline, ranging from data import to model design and training. It includes modules specifically designed to: split data at various granularity levels (e.g., session-, subject-, or dataset-based splits); effectively manage data stored with different configurations (e.g., file extensions, data types) during mini-batch construction; provide a wide range of standard deep learning models, data augmentations and SSL baseline methods applied to EEG data.
Most of the functionalities offered by selfEEG can be executed both on GPUs and CPUs, expanding its usability beyond the self-supervised learning area. Additionally, these functionalities can be employed for the analysis of other biomedical signals often coupled with EEGs, such as electromyography or electrocardiography data.
These features make selfEEG a versatile deep learning tool for biomedical applications and a useful resource in SSL, one of the currently most active fields of Artificial Intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05405 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography Del Pup, Federico Zanola, Andrea Tshimanga, Louis Fabrice Mazzon, Paolo Emilio Atzori, Manfredo Signal Processing Machine Learning SelfEEG is an open-source Python library developed to assist researchers in conducting Self-Supervised Learning (SSL) experiments on electroencephalography (EEG) data. Its primary objective is to offer a user-friendly but highly customizable environment, enabling users to efficiently design and execute self-supervised learning tasks on EEG data. SelfEEG covers all the stages of a typical SSL pipeline, ranging from data import to model design and training. It includes modules specifically designed to: split data at various granularity levels (e.g., session-, subject-, or dataset-based splits); effectively manage data stored with different configurations (e.g., file extensions, data types) during mini-batch construction; provide a wide range of standard deep learning models, data augmentations and SSL baseline methods applied to EEG data. Most of the functionalities offered by selfEEG can be executed both on GPUs and CPUs, expanding its usability beyond the self-supervised learning area. Additionally, these functionalities can be employed for the analysis of other biomedical signals often coupled with EEGs, such as electromyography or electrocardiography data. These features make selfEEG a versatile deep learning tool for biomedical applications and a useful resource in SSL, one of the currently most active fields of Artificial Intelligence. |
| title | SelfEEG: A Python library for Self-Supervised Learning in Electroencephalography |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2401.05405 |