Technology for visualizing dreams

Neural Decoding of Visual Imagery During Sleep
T. Horikawa,1,2 M. Tamaki,1* Y. Miyawaki,3,1† Y. Kamitani1,2‡
Visual imagery during sleep has long been a topic of persistent speculation, but its private
nature has hampered objective analysis. Here we present a neural decoding approach in which
machine-learning models predict the contents of visual imagery during the sleep-onset period,
given measured brain activity, by discovering links between human functional magnetic
resonance imaging patterns and verbal reports with the assistance of lexical and image databases.
Decoding models trained on stimulus-induced brain activity in visual cortical areas showed
accurate classification, detection, and identification of contents. Our findings demonstrate that
specific visual experience during sleep is represented by brain activity patterns shared by
stimulus perception, providing a means to uncover subjective contents of dreaming using
objective neural measurement.


About Tore Nielsen

Researcher at University of Montreal and Director of Dream & Nightmare Laboratory
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