@inproceedings{d08ad466350b4b6c90cba5de29b622f7,
title = "Classification from generation: Recognizing deep grammatical information during reading from rapid event-related fMRI",
abstract = "A novel fMRI classification method designed for rapid event related fMRI experiments is described and applied to the classification of loud reading of isolated words in Hebrew. Three comparisons of different grammatical complexity were performed: (i) words versus asterisks (ii) 'with diacritics versus without diacritics' and (iii) 'with root versus no root'. We discuss the most difficult task and, for comparison, the easiest one. Earlier work using more standard classification techniques (machine learning and statistical) succeeded fully only in the simplest of these tasks (i), but produced only partial results on (ii) and failed completely, even on the training set on the deepest task (iii).",
keywords = "Classification, Cognitive Processing, Functional magnetic resonance imaging (fMRI), Machine Learning, Multivoxel pattern analysis (MVPA), Neural Networks, Pattern Matching",
author = "Tali Bitan and Alex Frid and Hananel Hazan and Manevitz, \{Larry M.\} and Haim Shalelashvili and Yael Weiss",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727808",
language = "אנגלית",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4637--4642",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
address = "ארצות הברית",
}