braindecode.models.Deep4Net¶
- class braindecode.models.Deep4Net(in_chans, n_classes, input_window_samples, final_conv_length, n_filters_time=25, n_filters_spat=25, filter_time_length=10, pool_time_length=3, pool_time_stride=3, n_filters_2=50, filter_length_2=10, n_filters_3=100, filter_length_3=10, n_filters_4=200, filter_length_4=10, first_nonlin=<function elu>, first_pool_mode='max', first_pool_nonlin=<function identity>, later_nonlin=<function elu>, later_pool_mode='max', later_pool_nonlin=<function identity>, drop_prob=0.5, double_time_convs=False, split_first_layer=True, batch_norm=True, batch_norm_alpha=0.1, stride_before_pool=False)¶
Deep 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
