# Large Cognition Model: Towards Pretrained EEG Foundation Model

arXiv: https://arxiv.org/abs/2502.17464 · HTML: https://mlsanity.com/paper/2502.17464/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [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)
- [BIOT: Cross-data Biosignal Learning in the Wild](https://mlsanity.com/paper/2305.10351/) (2023)
- [QEEGNet: Quantum Machine Learning for Enhanced Electroencephalography Encoding](https://mlsanity.com/paper/2407.19214/) (2024)
- [Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning](https://mlsanity.com/paper/2406.16910/) (2024)
- [NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models](https://mlsanity.com/paper/2410.00712/) (2024)

## Built on by (most cited first, 10 of 10)

- [CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model](https://mlsanity.com/paper/2506.09110/) (2025)
- [Quantum Adaptive Self-Attention for Quantum Transformer Models](https://mlsanity.com/paper/2504.05336/) (2025)
- [Large Language Models for EEG: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2506.06353/) (2025)
- [EEGDM: EEG Representation Learning via Generative Diffusion Model](https://mlsanity.com/paper/2508.14086/) (2025)
- [ManifoldFormer: Geometric Deep Learning for Neural Dynamics on Riemannian Manifolds](https://mlsanity.com/paper/2511.16828/) (2025)
- [Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning](https://mlsanity.com/paper/2503.00080/) (2025)
- [Bridging Brain with Foundation Models through Self-Supervised Learning](https://mlsanity.com/paper/2506.16009/) (2025)
- [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025)
- [Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model](https://mlsanity.com/paper/2509.12497/) (2025)
- [GCMCG: A Clustering-Aware Graph Attention and Expert Fusion Network for Multi-Paradigm, Multi-task, and Cross-Subject EEG Decoding](https://mlsanity.com/paper/2512.00574/) (2025)

## Abstract (arXiv)

Electroencephalography provides a non-invasive window into brain activity, offering valuable insights for neurological research, brain-computer interfaces, and clinical diagnostics. However, the development of robust machine learning models for EEG analysis is hindered by the scarcity of large-scale, well-annotated datasets and the inherent variability of EEG signals across subjects and recording conditions. Inspired by the success of foundation models in natural language processing and computer vision, we propose the Large Cognition Model-a transformer-based foundation model designed to generalize across diverse EEG datasets and downstream tasks. Unlike traditional approaches, our proposed transformer-based architecture demonstrates strong generalization capabilities across datasets and tasks, even without pretraining, surpassing some existing EEG universal models on specific downstream applications. LCM leverages large-scale self-supervised learning techniques to capture universal EEG representations, enabling efficient fine-tuning for applications such as cognitive state decoding, disease classification, and neurofeedback systems. We introduce a novel architecture that integrates temporal and spectral attention mechanisms, optimizing the model's ability to extract meaningful features from raw EEG signals. Extensive evaluations demonstrate that LCM outperforms state-of-the-art approaches across multiple EEG benchmarks, exhibiting strong cross-subject and cross-task generalization. Our findings highlight the potential of pretrained EEG foundation models to accelerate advancements in neuroscience, personalized medicine, and BCI technology.