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Brain-grounding of semantic vectors improves neural decoding of visual stimuli
To address this issue, we propose a representation learning framework, termed brain-grounding of semantic vectors, which fine-tunes pretrained feature vectors to better align with the neural representation of visual stimuli in the human brain.
Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition
DMs can improve the accuracy of neural decoding when used with the nonlinear Grassmann kernel, compared to conventional power features.
Voluntary control of semantic neural representations by imagery with conflicting visual stimulation
The successful control of the feedback images demonstrated that the semantic vector inferred from electrocorticograms became closer to the vector of the imagined category, even while watching images from different categories.
