EEGPlain
A no-code cognitive EEG analysis platform where researchers describe their study question in plain English and get a reproducible MNE-Python pipeline with audit logs and statistical guardrails.
Cognitive neuroscience graduate students and PIs who do not want to write Python
- Plain-language question-to-pipeline translator that proposes contrasts, channels, time windows, and tests
- Pre-registered analysis templates for ERP and time-frequency studies with one-click run
- Audit log capturing every analysis choice so preregistrations stay clean
- Statistical guardrails that block adaptive peeking and flag p-curved results before release
The CogEEGAgent paper just showed LLMs can route EEG analysis choices more accurately than deterministic routers while staying fail-closed, and most labs still rely on grad-student code.
Real but narrow audience: cognitive neuroscientists without coding skills do exist, but the broader EEG community defaults to mature free tools (EEGLAB, MNE-Python, MNELAB) and the PyNoetic no-code framework was just published in PLOS ONE 2025 — indicating the pain point is recognized but already being addressed elsewhere.PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces ↗EEGLAB - University of California, San Diego ↗
Adjacent tools exist (PyNoetic for BCI no-code, MNELAB as MNE GUI, EEGLAB as MATLAB GUI) and competing research prototypes (EEGAgent, CogEEGAgent) are appearing on arXiv in late 2025 — so the plain-English → MNE pipeline niche is real but increasingly crowded.GitHub - cbrnr/mnelab: MNELAB - a GUI for MNE-Python ↗CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification ↗
Weak monetization: EEGLAB is GPL open source, MNE-Python is free, MNELAB is free — academic labs have strong free-tool norms and thin grant budgets; selling audit/reproducibility as a premium would require displacing entrenched free stacks, and PIs typically fund software through indirect costs rather than seat licenses.GitHub - sccn/eeglab: EEGLAB is an open source signal processing environment ↗GitHub - mne-tools/mne-python ↗
Strong longevity: EEG/cognitive neuroscience research is a decades-stable field with persistent reproducibility concerns, and LLM-assisted analysis is a multi-year research trend (EEGAgent Nov 2025, CogEEGAgent); demand for auditable pipelines will likely persist but LLM-based tools may face frequent model/UX turnover.EEGAgent: A Unified Framework for Automated EEG Analysis Using Large Language Models ↗CogEEGAgent: Toward Autonomous Cognitive EEG Analysis ↗
Moderate feasibility: wrapping MNE-Python is straightforward (it is mature and well-documented), but building robust statistical guardrails, reproducible audit logs, and reliable natural-language-to-pipeline routing requires substantial engineering beyond what existing prototypes (EEGAgent, PyNoetic) have shipped.MNE — MNE 1.12.1 documentation ↗GitHub - cbrnr/mnelab: MNELAB - a GUI for MNE-Python ↗