Classification of self-driven mental tasks from whole-brain activity patterns.
During wakefulness, a constant and continuous stream of complex stimuli and self-driven thoughts permeate the human mind. Here, eleven participants were asked to count down numbers and remember negative or positive autobiographical episodes of their personal lives, for 32 seconds at a time, during which they could freely engage in the execution of those tasks. We then examined the possibility of determining from a single whole-brain functional magnetic resonance imaging scan which one of the two mental tasks each participant was performing at a given point in time. Linear support-vector machines were used to build within-participant classifiers and across-participants classifiers. The within-participant classifiers could correctly discriminate scans with an average accuracy as high as 82%, when using data from all individual voxels in the brain. These results demonstrate that it is possible to accurately classify self-driven mental tasks from whole-brain activity patterns recorded in a time interval as short as 2 seconds.
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT) and Osaka University, Suita, Osaka, Japan; Universal Communication Research Institute, National Institute of Information and Communications Technology (NICT), Seika-cho, Soraku-gun, Kyoto, Japan.
Norberto Eiji
NE
Nawa
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology (NICT) and Osaka University, Suita, Osaka, Japan; Universal Communication Research Institute, National Institute of Information and Communications Technology (NICT), Seika-cho, Soraku-gun, Kyoto, Japan.
Hiroshi
H
Ando
D000161:Acoustic Stimulation; D001288:Attention; D001931:Brain Mapping / Q000379:methods; D002965:Classification / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D060388:Support Vector Machine; D013850:Thinking / Q000502:physiology*; D013997:Time Factors