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Decoding the individual finger movements from single‐trial functional magnetic resonance imaging recordings of human brain activity
Author(s) -
Shen Guohua,
Zhang Jing,
Wang Mengxing,
Lei Du,
Yang Guang,
Zhang Shanmin,
Du Xiaoxia
Publication year - 2014
Publication title -
european journal of neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.346
H-Index - 206
eISSN - 1460-9568
pISSN - 0953-816X
DOI - 10.1111/ejn.12547
Subject(s) - decoding methods , functional magnetic resonance imaging , primary motor cortex , brain–computer interface , somatosensory system , computer science , artificial intelligence , brain activity and meditation , pattern recognition (psychology) , magnetic resonance imaging , psychology , motor cortex , finger tapping , speech recognition , neuroscience , audiology , electroencephalography , medicine , telecommunications , stimulation , radiology
Multivariate pattern classification analysis ( MVPA ) has been applied to functional magnetic resonance imaging (f MRI ) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non‐invasively recorded human brain activation is crucial for implementing a brain–machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from f MRI single‐trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA , the decoding accuracy ( DA ) was computed separately for the different motor‐related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial–temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non‐invasive f MRI technique may provide informative features for decoding individual finger movements and the potential of developing an f MRI ‐based brain–machine interface for finger movement.