Deep Learning Decodes Imagined Sounds and Images From MEG, Topping 70% for Visual Imagery
Researchers recorded magnetoencephalography (MEG) from 18 right-handed participants as they imagined sounds and pictures, then compared two decoders: a convolutional neural network (CNN) and a linear logistic regression model. The CNN decoded both tasks above chance and exceeded 70% accuracy for visual imagery. It still decoded significantly when trained only on cortical regions unrelated to the task, suggesting imagined content is spread across partially overlapping networks rather than confined to a single sensory area, an experimental basis for feeding auditory and visual information into BCI decoders together.
Why it matters
By comparing imagined sounds and imagined images within one MEG dataset, the study shows their cortical representations overlap, with decodable information even in task-irrelevant regions; for BCI, that means future decoders need not fixate on a single sensory area and can let modalities complement each other, though with only 18 participants a usable system remains some way off.