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    This study introduces a novel deep learning architecture for decoding Electroencephalography (EEG) signals using language models, improving brain-computer interface (BCI) performance and comprehensibility.

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    Area of Science:

    • Neuroscience
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Pre-trained language models show promise for decoding Electroencephalography (EEG) signals via non-invasive Brain-Computer Interfaces (BCI).
    • The influence of embedding EEG signals within language models and subject-specific variations on decoding performance remains unclear.
    • Existing evaluation metrics for BCI decoding are primarily syntactic, lacking insight into the semantic comprehensibility of the output.

    Purpose of the Study:

    • To present an end-to-end deep learning architecture for open-vocabulary EEG decoding using modern representational learning.
    • To introduce a subject-dependent representation learning module, a BART language model, and a GPT-4 sentence refinement module for enhanced decoding.
    • To propose a comprehensive, sentence-level evaluation metric based on BERTScore and conduct an ablation study to analyze module contributions.

    Main Methods:

    • Developed an end-to-end deep learning architecture integrating subject-dependent EEG encoding, a BART language model, and GPT-4 for sentence refinement.
    • Utilized the BERTScore for a more comprehensive, sentence-level evaluation of decoded output quality.
    • Evaluated the proposed model on the ZuCo v1.0 and v2.0 datasets, comprising EEG recordings from 30 subjects during natural reading tasks.

    Main Results:

    • Achieved a BLEU-1 score of 42.75%, ROUGE-1-F of 33.28%, and BERTScore-F of 53.86% on the ZuCo datasets.
    • Demonstrated performance improvements over the previous state-of-the-art by 1.40% (BLEU-1), 2.59% (ROUGE-1-F), and 3.20% (BERTScore-F).
    • An ablation study provided insights into the specific contributions of each component within the proposed architecture.

    Conclusions:

    • The proposed end-to-end deep learning architecture effectively decodes open-vocabulary EEG signals, enhancing BCI performance.
    • The subject-dependent representation learning and GPT-4 refinement module significantly contribute to improved decoding accuracy and output comprehensibility.
    • The BERTScore offers a more robust evaluation metric for BCI decoding, aligning better with human understanding.