# From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining

arXiv: https://arxiv.org/abs/2512.15830 · HTML: https://mlsanity.com/paper/2512.15830/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 60 references that are in the map.
- First posted on arXiv 17 Dec 2025.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [Decoding speech perception from non-invasive brain recordings](https://mlsanity.com/paper/2208.12266/) (2022)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Parametric UMAP embeddings for representation and semi-supervised learning](https://mlsanity.com/paper/2009.12981/) (2020)
- [BrainBERT: Self-supervised representation learning for intracranial recordings](https://mlsanity.com/paper/2302.14367/) (2023)
- [Scaling laws for language encoding models in fMRI](https://mlsanity.com/paper/2305.11863/) (2023)
- [Brain decoding: toward real-time reconstruction of visual perception](https://mlsanity.com/paper/2310.19812/) (2023)
- [Toward a realistic model of speech processing in the brain with self-supervised learning](https://mlsanity.com/paper/2206.01685/) (2022)
- [Self-supervised models of audio effectively explain human cortical responses to speech](https://mlsanity.com/paper/2205.14252/) (2022)
- [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)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [LibriBrain: Over 50 Hours of Within-Subject MEG to Improve Speech Decoding Methods at Scale](https://mlsanity.com/paper/2506.02098/) (2025)
- [TRIBE: TRImodal Brain Encoder for whole-brain fMRI response prediction](https://mlsanity.com/paper/2507.22229/) (2025)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Decoding individual words from non-invasive brain recordings across 723 participants](https://mlsanity.com/paper/2412.17829/) (2024)
- [Sliding-Window Normalization to Improve the Performance of Machine-Learning Models for Real-Time Motion Prediction Using Electromyography](https://mlsanity.com/paper/2205.09301/) (2022)
- [Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI](https://mlsanity.com/paper/2505.14556/) (2025)
- [Emergence of Language in the Developing Brain](https://mlsanity.com/paper/2512.05718/) (2025)
- [Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings](https://mlsanity.com/paper/2410.12866/) (2024)
- [RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier](https://mlsanity.com/paper/2408.14477/) (2024)
- [How does artificial intelligence contribute to iEEG research?](https://mlsanity.com/paper/2207.13190/) (2022)

## Built on by (most cited first, 1 of 1)

- [Pretraining for Sample-Efficient Neural Interfaces](https://mlsanity.com/paper/2609.13507/) (2026)

## Abstract (arXiv)

Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to leverage week-long intracranial and audio recordings from patients undergoing clinical monitoring, effectively increasing the training dataset size by over two orders of magnitude. With this pretraining, our contrastive learning model substantially outperforms models trained solely on classic experimental data, with gains that scale log-linearly with dataset size. Analysis of the learned representations reveals that, while brain activity represents speech features, its global structure largely drifts across days, highlighting the need for models that explicitly account for cross-day variability. Overall, our approach opens a scalable path toward decoding and modeling brain representations in both real-life and controlled task settings.