title: Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.
abstract: Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) 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 fMRI 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 fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement.
authors.affiliation: Shanghai Key Laboratory of Magnetic Resonance, Department of Physics, East China Normal University, 3663 North Zhong-Shan Road, 200062, Shanghai, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Shen
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Mengxing
authors.initials: M
authors.lastname: Wang
authors.forename: Du
authors.initials: D
authors.lastname: Lei
authors.forename: Guang
authors.initials: G
authors.lastname: Yang
authors.forename: Shanmin
authors.initials: S
authors.lastname: Zhang
authors.forename: Xiaoxia
authors.initials: X
authors.lastname: Du
mesh_terms: D000293:Adolescent; D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D005385:Fingers / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D015999:Multivariate Analysis; D009483:Neuropsychological Tests; D010775:Photic Stimulation; D011930:Reaction Time; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014796:Visual Perception / Q000502:physiology; D055815:Young Adult
keywords: brain-machine interface; finger decoding; functional magnetic resonance imaging; motor cortex; multivariate pattern classification analysis