Evidence map›Paper›PMID 40768522›Full record

ArticlePloS one2025

PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces.

Gursimran Singh, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Gursimran SinghElectronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.ORCID https://orcid.org/0009-0006-7012-6970
Aviral ChhariaDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.ORCID https://orcid.org/0000-0003-4662-9747
Rahul UpadhyayElectronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Vinay KumarElectronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India.
Luca LongoArtificial Intelligence and Cognitive Load Lab, School of Computer Science, TU Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-2718-5426

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

BrainBrain-Computer InterfacesElectroencephalographySoftwareAlgorithmsHumansSignal Processing, Computer-Assisted

Identifiers

PMID40768522
PMCPMC12327672

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.