Name
Abstract | ||
|---|---|---|
Recent studies using functional magnetic resonance imaging (fMRI) have enabled quantitative evaluation of the semantic space during processing of visual stimuli. In the semantic space of the natural language processing model, called a skip-gram, decoders were shown to generalize to natural scenes of a movie that was not included in the training data of the decoders. Combined with electrocorticography (ECoG), which has a higher sampling rate than fMRI, this approach is expected to aid the development of a practical brain-machine interface. Here, we decoded vector representations of scenes within the semantic space of a skip-gram model to assess whether a decoder trained using ECoG features still generalizes to scenes new to the decoder. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/SMC.2018.00027 | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) |
Keywords | Field | DocType |
electrocorticography,decoding,skip-gram,semantic space | Training set,Functional magnetic resonance imaging,Electrocorticography,Computer science,Sampling (signal processing),Speech recognition,Artificial intelligence,Decoding methods,Stimulus (physiology),Visual perception,Machine learning,Semantic space | Conference |
ISSN | ISBN | Citations |
1062-922X | 978-1-5386-6651-7 | 0 |
PageRank | References | Authors |
0.34 | 2 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryohei Fukuma | 1 | 0 | 0.34 |
| Takufumi Yanagisawa | 2 | 21 | 6.27 |
| Shinji Nishimoto | 3 | 116 | 9.24 |
| Masataka Tanaka | 4 | 0 | 0.34 |
| Shota Yamamoto | 5 | 0 | 0.34 |
| Satoru Oshino | 6 | 16 | 3.09 |
| Yukiyasu Kamitani | 7 | 123 | 17.03 |
| Haruhiko Kishima | 8 | 20 | 5.00 |