The name is absent

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component, which was fit with a sum of two exponential functions y = ae^bx + ce^d*. The
function produced a good fit (mean r² = 0.996), with the slow linear component fit by
the first exponential and the rapid initial phase fit by the second exponential. The
number of voxels required to reach 75% of the rapid initial maximum was calculated as

Λ∕⅝= — Averaged across subjects, 8.3 voxels was required to reach 75% of the initial
α

maximum, with no significant difference between areas according to an ANOVA

(F(2,21)=0.9, p = 0.4).

C.MST∕STP

number Ofvoxels          number of voxels number Ofvoxels

Figure 5. Relationship between region of interest size and classification performance.

A. Classification accuracy for subsets of voxels from SI. Two-way classification (left hand vs.
right hand) was performed using randomly selected subsets of voxels. The у-axis shows the
classification accuracy for an ROI containing the number of voxels shown on the x-a×is. The
center gray line shows the mean performance across subjects, the shaded area shows +1SEM
across subjects (the color of the shaded area corresponds to the color used to illustrate the
corresponding ROI in Figure 2). The initial rise in the accuracy curve was fit with an
exponential function. The vertical bar in each curve shows the number of voxels required to
reach 75% of the peak of the exponential function.

B. Classification accuracy for subsets of voxels from S2.

C. Classification accuracy for subsets of voxels from MST∕STP

The results of experiment 1 demonstrated that MVPA could be used to decode
somatosensory stimuli widely separated on the body surface (left and right hand and

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