All implementations are not guaranteed to be correct, have not been checked by original authors, only reimplemented from the paper description.
Contains the original paper and model of EEGNet
It's a tensorflow implementation for EEGNet
for more information see https://github.com/vlawhern/arl-eegmodels
It's a tensorflow implementation for ConvNet
for more information see https://github.com/robintibor/braindevel
1. A trial contained 2s and was extraced 0.5s after the cue was given.
2. A 4-38Hz bandpass was done by a causal 6-order Butterworth fliter.
3. The MI dataset was sampled at 250Hz. And it was resampled to 128Hz for EEGnet.
tf_EEGNet just cannont converged on BCI_competion 2a even in the train set. And it would predit the same labels for all trials of test set and got a 25% acc.
Only resampling to 128Hz was done.
The P300 dataset was sampled at 2048Hz and contains 11 sujbect. Each trial is 0.5s long. This data set category is not balanced, has a proportion of 9.23 for 1 aginst 0.
tf_EEGNet did not have the ability to overfit on train set. After the unbalance_weights was added in the loss function, the prediction for test set would not be the same.
1. A trial contained 2s and was extraced 0.5s after the cue was given.
2. A 4-38Hz bandpass was done by a causal 6-order Butterworth fliter.
3. No resampling.
Got something hard to explain.
| Result | acc | mean |
|---|---|---|
| Train_acc | 0.4529,0.5208,0.4856,0.4886,0.5233,0.4891,0.5293,0.5109,0.4588 | 0.49548 |
| Best_val_acc | 0.4271,0.4896,0.4271,0.4167,0.4444,0.4583,0.4653,0.4236,0.3854 | 0.4375 |
| Earlystopping test_acc | 0.4965,0.2812,0.5278,0.3542,0.2222,0.3264,0.2812,0.5729,0.5660 | 0.40316 |
| Last_val_acc | 0.4201,0.4861,0.4167,0.4028,0.4340,0.4444,0.4583,0.4201,0.3819 | 0.42938 |
| Last_test_acc | 0.5104,0.2813,0.5208,0.3438,0.2188,0.3299,0.2847,0.5694,0.5660 | 0.40278 |