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Table 1 Results for GA-based feature selection.

From: Single-trial classification of NIRS signals during emotional induction tasks: towards a corporeal machine interface

Participant No.

Common features Selected Across

Performance Parameter Sets1

Optimal Parameter Set

  

Symbol2

Feature Pair

Classification Accuracy3

1

Mean, Skewness

LDA-L-20-

MeanHbOL 1

MeanHbOL 4

75.00 ± 10.83%

2

Mean, Skewness

LDA-L-20+

MeanHbOL 3

MeanHbOL 4

89.67 ± 7.82%

3

Mean, Skewness

LDA-L-20+

MeanHbOL 1

MeanHbOL 4

96.67 ± 5.32%

4

Kurtosis, Skewness

LDA-L-15-

KurtosisHbOL 4

SkewnessHbOL 3

75.33 ± 12.59%

5

Kurtosis, Skewness

LDA-L-15-

KurtosisHbOL 3

SkewnessHbL 2

88.00 ± 7.93%

6

Kurtosis, Skewness

SVM-L-20-

SkewnessHbOL 1

SkewnessHbOL 2

75.83 ± 10.55%

7

Mean

SVM-L-20+

MeanHbL 4

VarianceHbL 2

94.67 ± 5.77%

8

Mean, Skewness, Ea 6

LDA-R-20+

MeanHbR 3

ZCHbOR 3

89.00 ± 8.82%

9

Mean, Skewness

LDA-R-15+

EaHbR 3

SkewnessHbR 3

83.83 ± 9.88%

10

Mean, Skewness, E a

LDA-R-20+

Ed6HbOR 3

MeanHbOR 3

78.00 ± 9.78%

  1. 1Found in ≥25% feature pairs across performance parameter sets
  2. 2Symbol defining classification scheme consists of 4 parts: Classifier (LDA/SVM) - Recording Side (L/R) - Analysis Time Interval (15/20) - Stimulus Valence (+/-)
  3. 310 randomized trials, 5-fold cross-validation