Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing
arXiv:2505.10356v3 Announce Type: replace Abstract: Decoding language from the human brain remains a grand challenge for Brain-Computer Interfaces (BCIs). Current approaches typically rely on unimodal brain representations, neglecting the brain's inherently multimodal processing. Inspired by the brain's associative mechanisms, where viewing an image can evoke related sounds and linguistic representations, we propose a unified framework that leverages Multimodal Large Language Models (MLLMs) to align brain signals with a shared semantic space encompassing text, images, and audio. A router modul
The rapid advancements in Multimodal Large Language Models (MLLMs) are enabling unprecedented progress in bridging neuroscientific understanding with AI capabilities, making brain-to-text translation a more tangible reality.
This development represents a significant step towards direct communication with AI using thought, potentially revolutionizing human-computer interaction and addressing severe communication disabilities.
The ability to translate brain signals directly into multimodal outputs like text, images, and audio signifies a foundational change from traditional, unimodal brain-computer interfaces (BCIs).
- · Brain-Computer Interface industry
- · Patients with communication disorders
- · AI research and development
- · Neuroscience
- · Traditional unimodal BCI approaches
- · Companies reliant on slow or manual input methods
Direct brain-to-text translation becomes increasingly feasible and accurate, accelerating human-computer interaction.
This capability could lead to novel applications in design, education, and assistive technologies, driven purely by thought.
The blurring of lines between human thought and digital output raises profound ethical, privacy, and security questions about mental autonomy and AI interpretation.
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