Aug 20, 2023 · We provide a promising transfer learning framework for leveraging the capabilities of pre-trained vision and language models in EEG-based tasks.
In this paper, we demonstrate that large models (LMs) pre-trained from images as well as text can be fine-tuned for EEG-based prediction tasks without ...
Aug 20, 2023 · In this paper, we show that transformers pre-trained from images as well as text can be directly fine-tuned for EEG-based prediction tasks.
BENDR: Using Transformers and a Contrastive Self-Supervised Learning Task to Learn From Massive Amounts of EEG Data · Computer Science. Frontiers in Human ...
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In this paper, we show that transformers pre-trained from images as well as text can be directly fine-tuned for EEG-based prediction tasks. We design AdaCE, ...
Apr 15, 2024 · Pre-trained large transformer models have achieved remarkable performance in the fields of natural language processing and computer vision.
... large-scale EEG models can learn more generalizable EEG patterns, leading to improved performance on a wide range of downstream tasks in EEG analysis. Table ...
Transformer network has not only proved to be a resounding success in NLP, but it has also been successfully adapted to the field of reinforcement learning [5], ...
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Pre-trained large transformer models have achieved remarkable performance in the fields of natural language processing and computer vision.
Dec 19, 2024 · The CNN part learns local spatial representations of multi-channel EEG, while the sparse self-attention fusion with Transformers learns global ...