# BaRISTA: Brain Scale Informed Spatiotemporal Representation of Human Intracranial Neural Activity

arXiv: https://arxiv.org/abs/2512.12135 · HTML: https://mlsanity.com/paper/2512.12135/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 68 references that are in the map.
- First posted on arXiv 13 Dec 2025.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://mlsanity.com/paper/1706.02677/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [TS2Vec: Towards Universal Representation of Time Series](https://mlsanity.com/paper/2106.10466/) (2021)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking](https://mlsanity.com/paper/2409.19407/) (2024)
- [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)
- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [Linear dynamical neural population models through nonlinear embeddings](https://mlsanity.com/paper/1605.08454/) (2016)
- [Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution](https://mlsanity.com/paper/2407.14668/) (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)
- [Targeted Neural Dynamical Modeling](https://mlsanity.com/paper/2110.14853/) (2021)
- [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024)
- [Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli](https://mlsanity.com/paper/2411.08343/) (2024)
- [Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks](https://mlsanity.com/paper/1907.00650/) (2019)
- [BRAID: Input-Driven Nonlinear Dynamical Modeling of Neural-Behavioral Data](https://mlsanity.com/paper/2509.18627/) (2025)
- [Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data](https://mlsanity.com/paper/2509.18507/) (2025)
- [Learning Behavior Representations Through Multi-Timescale Bootstrapping](https://mlsanity.com/paper/2206.07041/) (2022)

## Built on by (most cited first, 7 of 7)

- [DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations](https://mlsanity.com/paper/2512.19097/) (2025)
- [Cross-Subject Modeling for Widefield Calcium Imaging via Atlas-Aligned Spatiotemporal Tokenization](https://mlsanity.com/paper/2607.09754/) (2026)
- [RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning](https://mlsanity.com/paper/2606.15278/) (2026)
- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [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)
- [PHASE: A Physiology-Guided Hierarchical Foundation Model for Intracranial EEG](https://mlsanity.com/paper/2609.36087/) (2026)

## Abstract (arXiv)

Intracranial recordings have opened a unique opportunity to simultaneously measure activity across multiregional networks in the human brain. Recent works have focused on developing transformer-based neurofoundation models of such recordings that can generalize across subjects and datasets. However, these recordings exhibit highly complex spatiotemporal interactions across diverse spatial scales, from the single-channel scale to the scale of brain regions. As such, there remain critical open questions regarding how best to encode spatial information and how to design self-supervision tasks that enable the learning of brain network patterns and enhance downstream decoding performance using such high-dimensional, multiregional recordings. To allow for exploring these questions, we propose a new spatiotemporal transformer model of multiregional neural activity and a corresponding self-supervised masked latent reconstruction task, designed to enable flexibility in the spatial scale used for token encoding and masking. Applying this model on publicly available multiregional intracranial electrophysiology (iEEG) data, we demonstrate that adjusting the spatial scale for both token encoding and masked reconstruction significantly impacts downstream decoding. Further, we find that spatial encoding at larger scales than channel-level encoding, which is commonly used in existing iEEG transformer models, improves downstream decoding performance. Finally, we demonstrate that our method allows for region-level token encoding while also maintaining accurate channel-level neural reconstruction. Taken together, our modeling framework enables exploration of the spatial scales used for token encoding and masking, reveals their importance towards self-supervised pretraining of neurofoundation models of multiregional human brain activity, and enhances downstream decoding performance.