  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
$\href{https://github.com/NeuSpeech/MAD-MEG2text}{https://github.com/NeuSpeech/MAD-MEG2text}$.
