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Sci Rep. 2016 Jun 2;6:27416. doi: 10.1038/srep27416.

Single-trial prediction of reaction time variability from MEG brain activity.

Ohata R1,2,3, Ogawa K1,4, Imamizu H1,3,5.

Author information

1
Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International (ATR), Keihanna Science City, Kyoto 619-0288, Japan.
2
Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka 565-0871, Japan.
3
Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Hongo 7-3-1, Bunkyou-ku, 113-0033 Japan.
4
Department of Psychology, Graduate School of Letters, Hokkaido University, Kita 10, Nishi 7, Kita-ku, Sapporo, 060-0810 Japan.
5
Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology and Osaka University, Suita, Osaka 565-0871, Japan.

Abstract

Neural activity prior to movement onset contains essential information for predictive assistance for humans using brain-machine-interfaces (BMIs). Even though previous studies successfully predicted different goals for upcoming movements, it is unclear whether non-invasive recording signals contain the information to predict trial-by-trial behavioral variability under the same movement. In this paper, we examined the predictability of subsequent short or long reaction times (RTs) from magnetoencephalography (MEG) signals in a delayed-reach task. The difference in RTs was classified significantly above chance from 550 ms before the go-signal onset using the cortical currents in the premotor cortex. Significantly above-chance classification was performed in the lateral prefrontal and the right inferior parietal cortices at the late stage of the delay period. Thus, inter-trial variability in RTs is predictable information. Our study provides a proof-of-concept of the future development of non-invasive BMIs to prevent delayed movements.

PMID:
27250872
PMCID:
PMC4889999
DOI:
10.1038/srep27416
[Indexed for MEDLINE]
Free PMC Article

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