PyTorch EEGNet Neural Network for BCI Signal Decoding
State-of-the-art EEGNet deep learning model implemented in PyTorch. Uses depthwise and separable convolutions for EEG signal classification across P300, Motor Imagery, and ERN tasks.
Appended directly to the LLM system prompt for tailored code output.
Generated Code
⚡ Instant Boilerplate| 1 | import torch |
| 2 | import torch.nn as nn |
| 3 | |
| 4 | class EEGNet(nn.Module): |
| 5 | def __init__(self, channels=8, samples=250, num_classes=2): |
| 6 | super().__init__() |
| 7 | self.conv1 = nn.Conv2d(1, 8, (1, 64), padding=(0, 32), bias=False) |
| 8 | self.depthwise = nn.Conv2d(8, 16, (channels, 1), groups=8, bias=False) |
| 9 | self.classifier = nn.Linear(16 * (samples // 8), num_classes) |
| 10 | |
| 11 | def forward(self, x): |
| 12 | return x |
| 13 |
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