Reconstructing perceived images based on brain signals measured with functional magnetic resonance imaging (fMRI) is a significant and meaningful task in brain-driven computer vision. However, the inconsistent distribution and representation between fMRI signals and visual images cause the heterogeneity gap, which makes it challenging to learn a reliable mapping between them. Moreover, considering that fMRI signals are extremely high-dimensional and contain a lot of visually-irrelevant information, effectively reducing the noise and encoding powerful visual representations for image reconstruction is also an open problem. We show that it is possible to overcome these challenges by learning a visually-relevant latent representation from fMRI signals guided by the corresponding visual features, and recovering the perceived images via adversarial learning. The resulting framework is called Dual-Variational Autoencoder Generative Adversarial Network (D-VAE GAN). By using a novel 3-stage training strategy, it encodes both cognitive and visual features via a dual structure variational autoencoder (D-VAE) to adapt cognitive features to visual feature space, and then learns to reconstruct perceived images with generative adversarial network (GAN). Extensive experiments on three fMRI recording datasets show that D-VAE GAN achieves more accurate visual reconstruction compared with the state-of-the-art methods.
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@cite_10: SUMMARY Perceptualexperienceconsistsofanenormousnumber of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as itisimpracticaltospecifybrainactivityforallpossible images.Inthisstudy,wereconstructedvisualimages by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binarycontrast, 10 3 10-patch images (2 100 possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. Theresultssuggestthatourapproachprovidesaneffective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.
@cite_11: Neural encoding and decoding provide perspectives for understanding neural representations of sensory inputs. Recent functional magnetic resonance imaging fMRI studies have succeeded in building prediction models for encoding and decoding numerous stimuli by representing a complex stimulus as a combination of simple elements. While arbitrary visual images were reconstructed using a modular model that combined the outputs of decoder modules for multiscale local image bases elements, the shapes of the image bases were heuristically determined. In this work, we propose a method to establish mappings between the stimulus and the brain by automatically extracting modules from measured data. We develop a model based on Bayesian canonical correlation analysis, in which each module is modeled by a latent variable that relates a set of pixels in a visual image to a set of voxels in an fMRI activity pattern. The estimated mapping from a latent variable to pixels can be regarded as an image basis. We show that the model estimates a modular representation with spatially localized multiscale image bases. Further, using the estimated mappings, we derive encoding and decoding models that produce accurate predictions for brain activity and stimulus images. Our approach thus provides a novel means of revealing neural representations of stimuli by automatically extracting modules, which can be used to generate effective prediction models for encoding and decoding.
Traditionally, machine learning methods play significant roles in fMRI-based brain decoding tasks. Miyawaki , for the first time, proposed spares multinomial logistic regression (SMLR) by using multi-voxel patterns of fMRI signals and multi-scale visual representation to reconstruct the lower-order information such as binary contrast patterns @cite_10 . Schoenmakers reconstructed handwritten characters using a straightforward linear Gaussian approach @cite_34 . Fujiwara proposed to build a reconstruction model in which image bases can be automatically estimated by Bayesian canonical correlation analysis (BCCA) @cite_11 . However, the linear hypothesis in the proposed model did not conform to the actual visual encoding-decoding process in human brain.