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arXiv · 2406.01512

MAD: Multi-Alignment MEG-to-Text Decoding

Yiqian Yang·Hyejeong Jo·Yiqun Duan·Qiang Zhang·Jinni Zhou·Xuming Hu·Won Hee Lee·Renjing Xu·Hui Xiong
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Abstract

Deciphering language from brain activity is a crucial task in brain-computer interface (BCI) research. Non-invasive cerebral signaling techniques including electroencephalography (EEG) and magnetoencephalography (MEG) are becoming increasingly popular due to their safety and practicality, avoiding invasive electrode implantation. However, current works under-investigated three points: 1) a predominant focus on EEG with limited exploration of MEG, which provides superior signal quality; 2) poor performance on unseen text, indicating the need for models that can better generalize to diverse linguistic contexts; 3) insufficient integration of information from other modalities, which could potentially constrain our capacity to comprehensively understand the intricate dynamics of brain activity. This study presents a novel approach for translating MEG signals into text using a speech-decoding framework with multiple alignments. Our method is the first to introduce an end-to-end multi-alignment framework for totally unseen text generation directly from MEG signals. We achieve an impressive BLEU-1 score on the \textit{GWilliams} dataset, significantly outperforming the baseline from 5.49 to 6.86 on the BLEU-1 metric. This improvement demonstrates the advancement of our model towards real-world applications and underscores its potential in advancing BCI research. Code is available at $\href{https://github.com/NeuSpeech/MAD-MEG2text}{https://github.com/NeuSpeech/MAD-MEG2text}$.

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Yiqian Yang, Hyejeong Jo, Yiqun Duan, Qiang Zhang, Jinni Zhou, Xuming Hu, Won Hee Lee, Renjing Xu, Hui Xiong. 2024-06-03. MAD: Multi-Alignment MEG-to-Text Decoding. https://arxiv.org/abs/2406.01512

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