Table 5 Classification accuracy using original combined EEG feature set against the optimal combined feature subset
Algorithm | EEG (linear classifier) | HRV (1-NN classifier) | Feature fusion (1-NN classifier) | Classifier fusion: linear classifier & 1-NN classifier | ||||
|---|---|---|---|---|---|---|---|---|
SEN | SPE | SEN | SPE | SEN | SPE | SEN | SPE | |
Proposed classifiers + optimal feature sets | 80.90 | 86.50 | 85.70 | 84.60 | 95.20 | 88.60 | 95.20 | 94.30 |
Proposed classifiers + full feature sets | 76.19 | 64.70 | 56.90 | 70.90 | 46.90 | 80.90 | 92.30 | 61.50 |