Zero-Code EEG Intelligence (Beta)

Vibe Coding for EEG — A ChatGPT-style EEG Assistant & Visual MLOps Pipeline Builder

Democratize EEG deep learning through a unified, zero-code platform. By combining a ChatGPT-style conversational AI assistant with an intuitive visual MLOps pipeline builder, NeuroBench Studio brings "vibe coding" to neural time-series data. It integrates EEGDash dataset streaming and Braindecode foundation models to make advanced analysis and model training effortlessly accessible to neuroscientists, clinicians, and machine learning researchers.

Built for Researchers & Clinicians

Empower your clinical investigations with visual data engineering and deep learning models.

MLOps Pipeline Canvas

Drag-and-drop datasets, temporal bandpass filters, re-referencing nodes, split strategies, and deep models. Adjust, re-run, and compare metrics instantly.

Conversational AI

An NLP-to-pipeline chat interface that answers signal quality questions, builds preprocessing routines, and generates dynamic multi-channel charts (e.g. "Visualize eeg1 live", "Is this patient epilepsy positive?", "How long is the EEG recording?", etc.).

Live EEG Stream Player

Downsamples raw waves to 32 Hz for lightweight rendering of records up to 1 hour, alongside 2D topographic power heatmaps and FFT brain metrics.

Interactive Platform Showcases

Watch short 1-3 minute screen recordings showing NeuroBench Studio in action.

MLOps Pipeline Canvas

Visual drag-and-drop neural network training and validation pipeline assembly.

Conversational AI Assistant

Natural language querying of signal quality and real-time report generation.

Live EEG Stream Player

Downsampled multi-channel wave visualization with interactive 2D scalp maps.

State-of-the-Art Deep Learning

Deploy 14 foundation neural network checkpoints instantly. Compare performance metrics on your local datasets or streams from EEGDash.

Supports 10+ foundation models in the EEG Assistant, and 60+ models in the drag-and-drop pipeline builder
Dynamic ROC-AUC curves and training curve visualization
Cohen's Kappa, Sensitivity, Specificity, and balanced accuracy metrics

How to Cite

If you use NeuroBench Studio in your research or clinical trials, please cite it as follows:

APA / IEEE Format

BibTeX Format

@misc{tegenaw2026neurobench,
  
}