Decoding the individual finger movements from single‐trial functional magnetic resonance imaging recordings of human brain activity

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.