MAD: Multi-Alignment MEG-to-Text Decoding

• URL: http://arxiv.org/abs/2406.01512v1
• Date: Mon, 3 Jun 2024 16:43:10 GMT
• Title: MAD: Multi-Alignment MEG-to-Text Decoding
• Authors: Yiqian Yang, Hyejeong Jo, Yiqun Duan, Qiang Zhang, Jinni Zhou, Won Hee Lee, Renjing Xu, Hui Xiong,
• Abstract summary: We present a novel approach for translating MEG signals into text using a speech-decoding framework with multiple alignments. We achieve an impressive BLEU-1 score on the $textitGWilliams$ dataset, significantly outperforming the baseline from 5.49 to 10.44 on the BLEU-1 metric.
• Score: 21.155031900491654
• License: http://creativecommons.org/licenses/by/4.0/
• 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 10.44 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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