braindecode.models.ShallowFBCSPNet

class braindecode.models.ShallowFBCSPNet(in_chans, n_classes, input_window_samples=None, n_filters_time=40, filter_time_length=25, n_filters_spat=40, pool_time_length=75, pool_time_stride=15, final_conv_length=30, conv_nonlin=<function square>, pool_mode='mean', pool_nonlin=<function safe_log>, split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1, drop_prob=0.5)

Shallow ConvNet model from Schirrmeister et al 2017.

Model described in [Schirrmeister2017].

Parameters

in_chansint

XXX

References

Schirrmeister2017

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730

Examples using braindecode.models.ShallowFBCSPNet

Regression example on fake data

Data Augmentation on BCIC IV 2a Dataset

Trialwise Decoding on BCIC IV 2a Dataset

Cropped Decoding on BCIC IV 2a Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Benchmarking eager and lazy loading

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset