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. Author manuscript; available in PMC: 2015 Aug 4.
Published in final edited form as: J Neural Eng. 2013 May 8;10(3):036019. doi: 10.1088/1741-2560/10/3/036019

Table A2.

Parameter threshold for prediction algorithm for ET.

Patient# Parameter with w1, w2 = 1 threshold
ET1 SpEn and P4¯(w1)
PmaxsEMG and FmaxsEMG(w1)
R(w2)
FmeansEMG(w2)
(ηl1, ηh1) = (0.25, 0.3); (ηl2, ηh2) = (0.18, 0.22); ηp = 10;
fp = 22; (fl, fh) = (5, 10)
(ρl, ρh) = (0.3, 0.35)
(fl, fh) = (10, 11)
ET2(left) PmaxsEMG and FmaxsEMG(w1) fp = 20; (fl, fh) = (4, 10)
R(w2) (ρl, ρh) = (0.3, 0.4)
ET2(right) SpEn and P4¯(w1)
FmeansEMG(w2)
(ηl1, ηh1) = (0.45, 0.5); (ηl2, ηh2) = (0.1, 0.2); ηp = 10; (fl, fh) = (11, 12)
ET3 SpEn and P4¯(w1)
R(w2)
(ηl1, ηh1) = (0.3, 0.4); (ηl2, ηh2) = (0.16, 0.18); ηp = 50;
(ρl, ρh) = (0.4, 0.48)
FmeansEMG(w2)
(fl, fh) = (11, 12)
ET4 SpEn and P4¯(w1)
PmaxsEMG and FmaxsEMG(w1)
(ηl1, ηh1) = (0.78, 0.98); (ηl2, ηh2) = (0.38, 0.48); ηp = 20;
fp = 22; (fl, fh) = (5, 10)
R(w2) (ρl, ρh) = (0.3, 0.34)