Papers with Contrastive EEG-Text Masked Autoencoder

1 papers
Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder (2024.acl-long)

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Challenge: EEG-based language decoding is still in its nascent stages, despite promising applications in brain-computer interfaces.
Approach: They propose a novel EEG-text Masked Autoencoder that orchestrates compound self-supervised learning across and within EEG and text through a dedicated multi-stream encoder.
Outcome: The proposed model outperforms baseline framework in ROUGE-1 F1 and BLEU-4 scores and an LLM (specifically BART) to improve downstream tasks involving EEG and text.

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