Decoding Visual Perception in the Brain in Real Time

Meta AI has proposed a model for decoding images in your brain as you think of them

Proposed architecture from Meta.

Understanding how the human brain represents and processes visual information remains one of the grand challenges of neuroscience. Recent advances in artificial intelligence (AI) and machine learning (ML) have unlocked new possibilities for modeling and decoding neural activity patterns underlying visual perception. In an exciting new study published in Nature, researchers from Meta AI and École Normale Supérieure push the boundaries of real-time visual decoding using magnetoencephalography (MEG).

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The Promise and Challenges of Real-Time Decoding

Brain-computer interfaces (BCIs) that can translate perceived or imagined content into text, images or speech hold tremendous potential for helping paralyzed patients communicate and interact with the world. Non-invasive BCIs based on electroencephalography (EEG) have enabled real-time decoding of speech and limited visual concepts. However, reliably decoding complex visual perceptions like natural images requires detecting fine-grained activity patterns across large cortical networks - a major challenge for low-resolution EEG.