Research Article
An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine
Table 2
Objective classification performance of ELM.
| Parameters | Evaluation metrics | Activation function | Sample point | Hidden layer nodes | Classification accuracy (%) | Average training time (s) | Average testing time (s) |
| sin | 1500 | 41 | 75.64 | 0.0009 | <0.0001 | 2000 | 58 | 80.77 | 0.0043 | 0.0012 | 2500 | 57 | 76.92 | 0.0055 | 0.0012 |
| sig | 1500 | 34 | 78.21 | 0.0040 | 0.0034 | 2000 | 49 | 79.49 | 0.0018 | 0.0018 | 2500 | 32 | 80.77 | 0.0006 | 0.0006 |
| hardlim | 1500 | 25 | 67.95 | 0.0018 | 0.0012 | 2000 | 50 | 78.21 | 0.0015 | 0.0031 | 2500 | 18 | 74.32 | 0.0012 | 0.0012 |
| tribas | 1500 | 40 | 67.95 | 0.0015 | 0.0049 | 2000 | 57 | 71.79 | 0.0015 | <0.0001 | 2500 | 53 | 69.23 | 0.0018 | 0.0012 |
| radbas | 1500 | 43 | 71.79 | 0.0021 | 0.0006 | 2000 | 58 | 69.23 | 0.0043 | 0.0006 | 2500 | 50 | 75.64 | 0.0012 | 0.0012 |
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