The invention discloses a complex visual image
reconstruction method based on a depth encoding and decoding
dual model and belongs to the visual scene reconstruction technology field in a
biomedical image brain decoding. The method is characterized by firstly, collecting and watching functional magnetic
resonance signals under a lot of natural images; and then establishing four network models: 1,a coding model, 2, a decoding model, 3, a natural image discrimination model and 4, a visual area response discrimination model, wherein in the coding model, a
convolutional neural network is used tocode the natural images into the
voxel signals of a visual area; in the decoding model, the
convolutional neural network and a
deconvolution neural network are used to decode the
voxel signals of thevisual area into the natural images; in the natural image discrimination model, true images and false images are discriminated; and in the a visual area response discrimination model, true signals andfalse signals are discriminated. Through training the four designed models, visual scene images can be recovered from a brain
signal. In the invention, for the first time, the problem of direct conversion between a natural scene and the brain
signal is solved, and the practical application of a brain-computer interface scene can be realized.