# Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

arXiv: https://arxiv.org/abs/2405.18765 · HTML: https://mlsanity.com/paper/2405.18765/

## Key facts

- Cited by 327 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 71 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 28 of 28)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://mlsanity.com/paper/2208.06366/) (2022)
- [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)
- [Uncovering the structure of clinical EEG signals with self-supervised learning](https://mlsanity.com/paper/2007.16104/) (2020)
- [Transformer-based Spatial-Temporal Feature Learning for EEG Decoding](https://mlsanity.com/paper/2106.11170/) (2021)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Transformer Convolutional Neural Networks for Automated Artifact Detection in Scalp EEG](https://mlsanity.com/paper/2208.02405/) (2022)
- [A review of feature extraction and performance evaluation in epileptic seizure detection using EEG](https://mlsanity.com/paper/1908.00492/) (2019)
- [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018)
- [Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task](https://mlsanity.com/paper/1910.10076/) (2019)
- [Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining](https://mlsanity.com/paper/2204.12440/) (2022)
- [EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks](https://mlsanity.com/paper/2306.13109/) (2023)
- [Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning](https://mlsanity.com/paper/2308.11636/) (2023)

## Built on by (most cited first, 30 of 327)

- [CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2412.07236/) (2024)
- [NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals](https://mlsanity.com/paper/2409.00101/) (2024)
- [REVE: A Foundation Model for EEG -- Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects](https://mlsanity.com/paper/2510.21585/) (2025)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model](https://mlsanity.com/paper/2502.10707/) (2025)
- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [EEG Foundation Models: A Critical Review of Current Progress and Future Directions](https://mlsanity.com/paper/2507.11783/) (2025)
- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals](https://mlsanity.com/paper/2505.18185/) (2025)
- [Population Transformer: Learning Population-level Representations of Neural Activity](https://mlsanity.com/paper/2406.03044/) (2024)
- [LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis](https://mlsanity.com/paper/2510.22257/) (2025)
- [Neuro-3D: Towards 3D Visual Decoding from EEG Signals](https://mlsanity.com/paper/2411.12248/) (2024)
- [EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models](https://mlsanity.com/paper/2508.17742/) (2025)
- [Brain Foundation Models: A Survey on Advancements in Neural Signal Processing and Brain Discovery](https://mlsanity.com/paper/2503.00580/) (2025)
- [The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning](https://mlsanity.com/paper/2406.04328/) (2024)
- [Brant-X: A Unified Physiological Signal Alignment Framework](https://mlsanity.com/paper/2409.00122/) (2024)
- [Tokenizing Single-Channel EEG with Time-Frequency Motif Learning](https://mlsanity.com/paper/2502.16060/) (2025)
- [AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applications](https://mlsanity.com/paper/2507.09882/) (2025)
- [FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling](https://mlsanity.com/paper/2409.12454/) (2024)
- [FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model](https://mlsanity.com/paper/2502.06438/) (2025)
- [NeuralBench: A Unifying Framework to Benchmark NeuroAI Models](https://mlsanity.com/paper/2605.08495/) (2026)
- [Unlocking Non-Invasive Brain-to-Text](https://mlsanity.com/paper/2505.13446/) (2025)
- [Are Large Brainwave Foundation Models Capable Yet? Insights from Fine-tuning](https://mlsanity.com/paper/2507.01196/) (2025)
- [CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention](https://mlsanity.com/paper/2501.10885/) (2025)
- [Neural Codecs as Biosignal Tokenizers](https://mlsanity.com/paper/2510.09095/) (2025)
- [EEG-Bench: A Benchmark for EEG Foundation Models in Clinical Applications](https://mlsanity.com/paper/2512.08959/) (2025)
- [PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation](https://mlsanity.com/paper/2506.10351/) (2025)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [NeuroBOLT: Resting-state EEG-to-fMRI Synthesis with Multi-dimensional Feature Mapping](https://mlsanity.com/paper/2410.05341/) (2024)

## Abstract (arXiv)

The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text processing, prompting us to explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can break through the limitations of different task types of EEG datasets, and obtain universal perceptual capabilities of EEG signals through unsupervised pre-training. Then the models can be fine-tuned for different downstream tasks. However, compared to text data, the volume of EEG datasets is generally small and the format varies widely. For example, there can be mismatched numbers of electrodes, unequal length data samples, varied task designs, and low signal-to-noise ratio. To overcome these challenges, we propose a unified foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables cross-dataset learning by segmenting the EEG signals into EEG channel patches. Vector-quantized neural spectrum prediction is used to train a semantically rich neural tokenizer that encodes continuous raw EEG channel patches into compact neural codes. We then pre-train neural Transformers by predicting the original neural codes for the masked EEG channel patches. The LaBraMs were pre-trained on about 2,500 hours of various types of EEG signals from around 20 datasets and validated on multiple different types of downstream tasks. Experiments on abnormal detection, event type classification, emotion recognition, and gait prediction show that our LaBraM outperforms all compared SOTA methods in their respective fields. Our code is available at https://github.com/935963004/LaBraM.