PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.
Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility es...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327791 |
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| author | Gursimran Singh Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo |
| author_facet | Gursimran Singh Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo |
| author_sort | Gursimran Singh |
| collection | DOAJ |
| description | Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors. |
| format | Article |
| id | doaj-art-2aff0a7fcabe4944884599b0f33acce9 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2aff0a7fcabe4944884599b0f33acce92025-08-23T05:32:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032779110.1371/journal.pone.0327791PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.Gursimran SinghAviral ChhariaRahul UpadhyayVinay KumarLuca LongoElectroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors.https://doi.org/10.1371/journal.pone.0327791 |
| spellingShingle | Gursimran Singh Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. PLoS ONE |
| title | PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. |
| title_full | PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. |
| title_fullStr | PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. |
| title_full_unstemmed | PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. |
| title_short | PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces. |
| title_sort | pynoetic a modular python framework for no code development of eeg brain computer interfaces |
| url | https://doi.org/10.1371/journal.pone.0327791 |
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