Research Article
Epilepsy Detection in EEG Using Grassmann Discriminant Analysis Method
Table 2
The performance of FMGDA algorithms in the epilepsy detection of five categories.
| Ratio | | | | | | | | | | | | | | |
| 0.1 | 0.3800 | 0.3822 | 0.3689 | 0.4667 | 0.4489 | 0.4156 | 0.4289 | 0.3756 | 0.4022 | 0.3867 | 0.3733 | 0.3356 | 0.2622 | 0.2267 | 0.2 | 0.4050 | 0.3850 | 0.3725 | 0.4250 | 0.4525 | 0.4450 | 0.4575 | 0.4275 | 0.3950 | 0.4000 | 0.3750 | 0.3325 | 0.3175 | 0.2475 | 0.4 | 0.4233 | 0.3600 | 0.4367 | 0.4600 | 0.4733 | 0.4933 | 0.5400 | 0.4876 | 0.4500 | 0.4667 | 0.4200 | 0.3700 | 0.3100 | 0.2433 | 0.6 | 0.5150 | 0.3750 | 0.4000 | 0.4900 | 0.5000 | 0.4750 | 0.6200 | 0.5600 | 0.4650 | 0.4500 | 0.4100 | 0.3800 | 0.2600 | 0.2700 | 0.8 | 0.4600 | 0.4100 | 0.5000 | 0.5700 | 0.5300 | 0.5800 | 0.5900 | 0.5000 | 0.5200 | 0.4750 | 0.4300 | 0.3600 | 0.3300 | 0.2200 |
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