# Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli

arXiv: https://arxiv.org/abs/2509.21671 · HTML: https://mlsanity.com/paper/2509.21671/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 65 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data](https://mlsanity.com/paper/2403.11207/) (2024)
- [Natural scene reconstruction from fMRI signals using generative latent diffusion](https://mlsanity.com/paper/2303.05334/) (2023)
- [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023)
- [Brain decoding: toward real-time reconstruction of visual perception](https://mlsanity.com/paper/2310.19812/) (2023)
- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (2024)
- [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023)
- [BrainWave: A Brain Signal Foundation Model for Clinical Applications](https://mlsanity.com/paper/2402.10251/) (2024)
- [Population Transformer: Learning Population-level Representations of Neural Activity](https://mlsanity.com/paper/2406.03044/) (2024)
- [STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer](https://mlsanity.com/paper/2206.04727/) (2022)
- [Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli](https://mlsanity.com/paper/2411.08343/) (2024)
- [Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals](https://mlsanity.com/paper/2405.11459/) (2024)
- [Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data](https://mlsanity.com/paper/2311.00136/) (2023)
- [ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish](https://mlsanity.com/paper/2503.02618/) (2025)

## Built on by (most cited first, 3 of 3)

- [DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations](https://mlsanity.com/paper/2512.19097/) (2025)
- [Pretraining for Sample-Efficient Neural Interfaces](https://mlsanity.com/paper/2609.13507/) (2026)
- [iMINDBench: iEEG Multi-Institution Neural Decoding Benchmark](https://mlsanity.com/paper/2609.18104/) (2026)

## Abstract (arXiv)

High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmarks to discriminate between competing modeling approaches, yet no standardized evaluation frameworks exist for intracranial EEG (iEEG) recordings. To address this gap, we present Neuroprobe: a suite of decoding tasks for studying multi-modal language processing in the brain. Unlike scalp EEG, intracranial EEG requires invasive surgery to implant electrodes that record neural activity directly from the brain with minimal signal distortion. Neuroprobe is built on the BrainTreebank dataset, which consists of over 40 hours of iEEG recordings from 10 human subjects performing a naturalistic movie viewing task. Neuroprobe serves two critical functions. First, it is a source from which neuroscience insights can be drawn. The high temporal and spatial resolution of the labeled iEEG allows researchers to systematically determine when and where computations for each aspect of language processing occur in the brain by measuring the decodability of each feature across time and all electrode locations. Using Neuroprobe, we visualize how information flows from key language and audio processing sites in the superior temporal gyrus to sites in the prefrontal cortex. We also demonstrate the time evolution of processing from simple auditory features (e.g., pitch and volume) to more complex language features (e.g., part of speech) in a purely data-driven manner. Second, as the field moves toward neural foundation models trained on large-scale datasets, Neuroprobe provides a rigorous framework for comparing competing architectures and training protocols. We make the code for Neuroprobe openly available, aiming to enable rapid progress in the field of iEEG foundation models. Public leaderboard: https://neuroprobe.dev/