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- W4312711912 abstract "With the development in brain decoding and encoding, the seen and imagery object identification tasks achieve increasingly higher accuracy by using more intricate features. However, the experiment always requires a huge amount of data to process which will consume a lot of time. With available seen and imagery BOLD signals, we leverage the Generic Brain Decoding model to fit and then predict visual feature vectors extracted from neural networks. Given the hierarchy of AlexNet and that of the human visual cortex, the accuracies of the couplings between low layers of AlexNet and human visual cortex and those between high layers outperform other coupling cases. Using a different number of feature dimensions during feature selection induces a significant difference in the decoding performance at the high layers. Moreover, the shallow neural network achieves higher accuracies at the high layers. The results demonstrate that when the number of available data samples is not sufficient, reducing the input feature dimensions can still ensure a comparable decoding accuracy." @default.
- W4312711912 created "2023-01-05" @default.
- W4312711912 creator A5018054686 @default.
- W4312711912 date "2022-05-06" @default.
- W4312711912 modified "2023-09-26" @default.
- W4312711912 title "On the Influence of Feature Selection and Regression Models on the Decoding Accuracy of Seen and Imagery Objects Using Hierarchical Visual Features" @default.
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- W4312711912 doi "https://doi.org/10.1145/3560470.3560472" @default.
- W4312711912 hasPublicationYear "2022" @default.
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