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Table 2 Classification accuracy for each subject.

From: Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

Best-performing combination

Subject No.

Channel

Time interval

Optimal feature set

Classification accuracy

1

3

9-15 s

Δ[O2Hb] mean, variance, skewness, kurtosis

91.7%

2

2

5-15 s

Δ[O2Hb] mean, variance

79.2%

3

3

9-15 s

Δ[O2Hb] variance, skewness, kurtosis

79.2%

4

2

8-14 s

Δ[O2Hb] mean, variance

75.0%

5

3

9-15 s

Δ[O2Hb] mean

75.0%

6

3

7-15 s

Δ[O2Hb] mean, variance, skewness

91.7%

7

1

8-14 s

Δ[O2Hb] skewness

70.8%

8

2

7-12 s

Δ[O2Hb] mean, variance

75.0%

9

1

5-15 s

Δ[O2Hb] mean, variance

83.3%

10

3

5-15 s

Δ[O2Hb]variance, skewness, kurtosis

87.5%

11

3

7-15 s

Δ[O2Hb]variance, kurtosis

87.5%

12

2

11-15 s

Δ[O2Hb] mean, variance, skewness, kurtosis

79.2%

Overall

   

81.3 ± 7.0%

  1. The results are shown for the best-performing combination of one channel, a certain time interval and the optimal feature set for each subject. Classification accuracy was identified over 12 randomised trials by cross validation. Four features were used: mean Δ[O2Hb] amplitude, variance, skewness and kurtosis