An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface
Table 1
Performance of both calibrations when the classifier is calibrated with a different number of blocks.
Subjects
Adaptive calibrationby SVM and fCM (accuracy (%)/ITR)
SVM
4
6
9
S1
86.1/13.4
81.5/11.7
83.3/12.4
83.3/12.4
S2
94.4/17.1
94.4/17.1
91.7/15.8
91.7/15.8
S3
69.4/7.9
72.2/8.7
72.2/8.7
66.7/7.2
S4
94.4/17.1
97.2/18.7
94.4/17.1
91.7/15.8
S5
77.8/10.4
80.6/11.4
77.8/10.4
75/9.5
S6
91.7/15.8
88.9/14.6
88.9/14.6
88.9/14.6
S7
94.4/17.1
94.4/17.1
91.7/15.8
91.7/15.8
S8
94.4/17.1
94.4/17.1
91.7/15.8
88.9/14.6
S9
94.4/17.1
94.4/17.1
94.4/17.1
94.4/17.1
S10
83.3/12.4
77.8/10.4
80.6/11.4
77.8/10.4
S11
91.7/15.8
94.4/17.1
94.4/17.1
91.7/15.8
Mean ± std
/
/
/
/
denotes that the adaptive calibration method results are significantly higher than those of SVM approach (, paired -test). In each column, the left and right values of “/” denote the accuracies and ITRs of subjects, respectively.
