braindecode.models.EEGTCNet#
- class braindecode.models.EEGTCNet(n_chans=None, n_outputs=None, n_times=None, chs_info=None, input_window_seconds=None, sfreq=None, activation=<class 'torch.nn.modules.activation.ELU'>, depth_multiplier=2, filter_1=8, kern_length=64, drop_prob=0.5, depth=2, kernel_size=4, filters=12, max_norm_const=0.25)[source]#
EEGTCNet model from Ingolfsson et al (2020) [ingolfsson2020].
Convolution Recurrent
Combining EEGNet and TCN blocks.
- Parameters:
activation (
type[Module]) – Activation function to use. Default is nn.ELU().depth_multiplier (
int) – Depth multiplier for the depthwise convolution. Default is 2.filter_1 (
int) – Number of temporal filters in the first convolutional layer. Default is 8.kern_length (
int) – Length of the temporal kernel in the first convolutional layer. Default is 64.dropout (float, optional) – Dropout rate. Default is 0.5.
depth (
int) – Number of residual blocks in the TCN. Default is 2.kernel_size (
int) – Size of the temporal convolutional kernel in the TCN. Default is 4.filters (
int) – Number of filters in the TCN convolutional layers. Default is 12.max_norm_const (
float) – Maximum L2-norm constraint imposed on weights of the last fully-connected layer. Defaults to 0.25.
References
[ingolfsson2020]Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., & Benini, L. (2020). EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces. https://doi.org/10.48550/arXiv.2006.00622
Methods