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Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding
The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series in a predefined locality.
Learning Deep Temporal Representations for Brain Decoding
By employing the proposed temporal convolutional architecture with spatial pooling, raw input fMRI data is mapped to a non-linear, highly-expressive and low-dimensional feature space where the final classification is conducted.
Discriminative Functional Connectivity Measures for Brain Decoding
The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories.
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