Table 4 Comparison of classification accuracy of proposed methodology with existing techniques.
From: Deep temporal networks for EEG-based motor imagery recognition
Sr. no. | Author | Methodology | Dataset | Accuracy (%) |
|---|---|---|---|---|
1 | Dai et al.13 | Transfer kernel CSP | BCI III IVa | 91.2 |
2 | Taheri et al.18 | CNN/ReLU | BCI III IVa | 96.34 |
3 | Song et al.20 | Transformer | BCI IV 2a | 84.2 |
BCI IV 2b | 82.59 | |||
4 | Yongkoo et al.42 | CSP feature | BCI III IVa | 84.4 |
5 | Ma et al.43 | CNN-transformer | BCI IV 2a | 83.9 |
6 | Zhang et al.44 | CNN/LSTM | BCI IV 2a | 83 |
7 | Proposed methodology | BCI III IVa | 99.5 | |
| Â | Â | BCI IV 2a | 84 | |
