Name
Title | ||
|---|---|---|
Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression. |
Abstract | ||
|---|---|---|
Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generalized is not clear. This study addresses the challenge of producing generalizable decoding models, which allow the reconstruction of perceived audiovisual features from human magnetic resonance imaging (fMRI) data without prior training of the algorithm on the decoded content. We applied an adapted version of kernel ridge regression combined with temporal optimization on data acquired during film viewing (234 runs) to generate standardized brain models for sound loudness, speech presence, perceived motion, face-to-frame ratio, lightness, and color brightness. The prediction accuracies were tested on data collected from different subjects watching other movies mainly in another scanner. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.neuroimage.2017.09.032 | NeuroImage |
Keywords | Field | DocType |
fMRI,Audiovisual decoding,Motion pictures,Kernel ridge regression,Sound loudness,Optical flow,Face,Motion pictures | Generalizability theory,Loudness,Psychology,Robustness (computer science),Speech recognition,Neural decoding,Lightness,Decoding methods,Optical flow,Brightness | Journal |
Volume | ISSN | Citations |
163 | 1053-8119 | 1 |
PageRank | References | Authors |
0.35 | 20 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gal Raz | 1 | 2 | 1.73 |
| M. Svanera | 2 | 13 | 2.26 |
| Neomi Singer | 3 | 5 | 1.52 |
| Gadi Gilam | 4 | 16 | 1.93 |
| Maya Bleich Cohen | 5 | 1 | 0.35 |
| Tamar Lin | 6 | 3 | 1.07 |
| Roee Admon | 7 | 2 | 0.71 |
| Tal Gonen | 8 | 2 | 0.74 |
| Avner Thaler | 9 | 1 | 0.35 |
| Roni Y. Granot | 10 | 2 | 0.71 |
| rainer goebel | 11 | 476 | 40.13 |
| Sergio Benini | 12 | 228 | 19.81 |
| Giancarlo Valente | 13 | 127 | 10.62 |