Bayesian reconstruction of perceptual experiences from human brain activity
Frontiers in Computational Neuroscience(2009)
摘要
A method for decoding the subjective contents of perceptual systems in the human brain would have broad practical utility
for communication and as a brain-machine interface. Previous approaches to this problem in vision have used linear classifiers
to solve specific problems, but these approaches were not general enough to solve complex problems such as reconstructing
subjective perceptual states. We have developed a new approach to these problems based on quantitative encoding models that
explicitly describe how visual stimuli are (nonlinearly) transformed into brain activity. We then invert these encoding models
in order to decode activity evoked by novel images or movies, providing reconstructions with unprecedented fidelity. Here
we briefly review these results and the potential uses of perceptual decoding devices.
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关键词
brain-machine interface,human brain,quantitative encoding model,subjective content,perceptual system,perceptual decoding device,bayesian reconstruction,perceptual experiences,encoding model,subjective perceptual state,brain activity,human brain activity,broad practical utility,vision,bayesian,brain computer interface,brain machine interface
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