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EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model
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Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision. It is also applicable to brain signals such as electroencephalography (EEG) data, given the abundance of available unlabeled data that exist in a wide spectrum of real-world medical applications ranging from seizure detection to wave analysis. The existing works leveraging self-supervised learning on EEG modeling mainly focus on pretraining upon each individual dataset corresponding to a single downstream task, which cannot leverage the power of abundant data, and they may derive sub-optimal solutions with a lack of generalization. Moreover, these methods rely on end-to-end model learning which is not easy for humans to understand. In this paper, we present a novel EEG foundation model, namely EEGFormer, pretrained on large-scale compound EEG data. The pretrained model cannot only learn universal representations on EEG signals with adaptable performance on various downstream tasks but also provide interpretable outcomes of the useful patterns within the data. To validate the effectiveness of our model, we extensively evaluate it on various downstream tasks and assess the performance under different transfer settings. Furthermore, we demonstrate how the learned model exhibits transferable anomaly detection performance and provides valuable interpretability of the acquired patterns via self-supervised learning.
Forward citations
Cited by 7 Pith papers
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DIVER-1: Scaling Intracranial EEG Foundation Models for Transferable Representations
DIVER-1 shows data-constrained scaling, not parameter count, governs EEG/iEEG foundation-model performance: a 51M-parameter model trained 32 epochs beats a 1.8B model trained 8 epochs at lower compute.
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A Silent Speech Decoding System from EEG and EMG with Heterogenous Electrode Configurations
A multi-task neural net pre-trained on 220 hours of heterogeneous EEG/EMG decodes silent speech words at 95.3% accuracy in healthy users and 54.5% in one patient, beating single-subject baselines.
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DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations
A diffusion U-Net pretrained on unlabeled TUHSZ EEG plus an F1-maximizing RL decision layer yields clinically usable patient-wise seizure detection and subtyping under severe class imbalance.
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CRIA: A Cross-View Interaction and Instance-Adapted Pre-training Framework for Generalizable EEG Representations
Fusing temporal, spectral, and spatial EEG views with cross-attention and view-wise masking improves downstream classification and cross-dataset generalization over prior EEG pretraining models.
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From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data
Fine-tuning the LaBraM EEG foundation model on 82 real-world classroom stress recordings yields 90.47% balanced accuracy in the best of four seeds, but the tiny, seed-dependent test set makes the headline fragile.
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Large Language Models for EEG: A Comprehensive Survey and Taxonomy
A taxonomy and review of studies applying large language models to EEG signals, organized into four domains and three adaptation strategies.
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Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review
This paper is a survey: it organizes existing foundation-model work in neuroscience into five application domains and lists public datasets, without presenting new experiments.
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