Generic decoding of seen and imagined objects using hierarchical visual features
Machine learning algorithms can decode objects that people see or imagine from their brain activity. Here the authors present a predictive decoder combined with deep neural network representations that generalizes beyond the training set and correctly identifies novel objects that it has never been...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2017-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/ncomms15037 |
| Summary: | Machine learning algorithms can decode objects that people see or imagine from their brain activity. Here the authors present a predictive decoder combined with deep neural network representations that generalizes beyond the training set and correctly identifies novel objects that it has never been trained on. |
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| ISSN: | 2041-1723 |