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| Main Authors: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Subjects: | |
| Acceso en liña: | https://arxiv.org/abs/2411.17721 |
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| _version_ | 1866908150069723136 |
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| author | Delorme, Arnaud Truong, Dung Pion-Tonachini, Luca Makeig, Scott |
| author_facet | Delorme, Arnaud Truong, Dung Pion-Tonachini, Luca Makeig, Scott |
| contents | ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17721 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic EEG Independent Component Classification Using ICLabel in Python Delorme, Arnaud Truong, Dung Pion-Tonachini, Luca Makeig, Scott Signal Processing Machine Learning Neurons and Cognition ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decomposing the data by Independent Component Analysis (ICA) and then classifying the resulting independent components (ICs) using ICLabel. While EEGLAB pipelines support high-performance computing (HPC) platforms running the open-source Octave interpreter, the ICLabel plug-in is incompatible with Octave because of its specialized neural network architecture. To enhance cross-platform compatibility, we developed a Python version of ICLabel that uses standard EEGLAB data structures. We compared ICLabel MATLAB and Python implementations to data from 14 subjects. ICLabel returns the likelihood of classification in 7 classes of components for each ICA component. The returned IC classifications were virtually identical between Python and MATLAB, with differences in classification percentage below 0.001%. |
| title | Automatic EEG Independent Component Classification Using ICLabel in Python |
| topic | Signal Processing Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2411.17721 |