From: Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing
Architecture | Maxpool | Temporal convolution | LSTM | Mixed LSTM (source) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
\(\epsilon\) | Evaluation Criteria | Acc | SR | Acc | SR | Acc | SR | Acc | SR | \({\mathrm{DL}}_{B}\) | \({\mathrm{DL}}_{E}\) |
- | - | 0.92 | - | 0.92 | - | 0.93 | - | 0.94 | - | - | - |
0.1 | \(\mathrm{GPBEAM}\) | 0.80 | 0.15 | 0.79 | 0.14 | 0.80 | 0.15 | 0.73 | 0.20 | 0.11 | 0.024 |
GPBEAM-DE | 0.83 | 0.13 | 0.76 | 0.18 | 075 | 0.19 | 0.64 | 0.31 | 0.06 | 0.021 | |
0.3 | \(\mathrm{GPBEAM}\) | 0.67 | 0.27 | 0.66 | 0.28 | 0.65 | 0.29 | 0.61 | 0.32 | 0.32 | 0.030 |
GPBEAM-DE | 0.46 | 0.48 | 0.39 | 0.52 | 0.44 | 0.49 | 0.25 | 0.70 | 0.18 | 0.026 | |
0.5 | \(\mathrm{GPBEAM}\) | 0.62 | 0.32 | 0.60 | 0.33 | 0.61 | 0.32 | 0.56 | 0.37 | 0.53 | 0.036 |
GPBEAM-DE | 0.36 | 0.60 | 0.32 | 0.66 | 0.35 | 0.62 | 0.15 | 0.80 | 0.29 | 0.030 |