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:
n_chans (int) – Number of EEG channels.
n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.
n_times (int) – Number of time samples of the input window.
chs_info (list of dict) – Information about each individual EEG channel. This should be filled with
info["chs"]. Refer tomne.Infofor more details.input_window_seconds (float) – Length of the input window in seconds.
sfreq (float) – Sampling frequency of the EEG recordings.
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.drop_prob (
float) – The description is missing.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.
- Raises:
ValueError – If some input signal-related parameters are not specified: and can not be inferred.
Notes
If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.
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
Hugging Face Hub integration
When the optional
huggingface_hubpackage is installed, all models automatically gain the ability to be pushed to and loaded from the Hugging Face Hub. Install with:pip install braindecode[hub]
Pushing a model to the Hub:
from braindecode.models import EEGTCNet # Train your model model = EEGTCNet(n_chans=22, n_outputs=4, n_times=1000) # ... training code ... # Push to the Hub model.push_to_hub( repo_id="username/my-eegtcnet-model", commit_message="Initial model upload", )
Loading a model from the Hub:
from braindecode.models import EEGTCNet # Load pretrained model model = EEGTCNet.from_pretrained("username/my-eegtcnet-model") # Load with a different number of outputs (head is rebuilt automatically) model = EEGTCNet.from_pretrained("username/my-eegtcnet-model", n_outputs=4)
Extracting features and replacing the head:
import torch x = torch.randn(1, model.n_chans, model.n_times) # Extract encoder features (consistent dict across all models) out = model(x, return_features=True) features = out["features"] # Replace the classification head model.reset_head(n_outputs=10)
Saving and restoring full configuration:
import json config = model.get_config() # all __init__ params with open("config.json", "w") as f: json.dump(config, f) model2 = EEGTCNet.from_config(config) # reconstruct (no weights)
All model parameters (both EEG-specific and model-specific such as dropout rates, activation functions, number of filters) are automatically saved to the Hub and restored when loading.
See Loading and Adapting Pretrained Foundation Models for a complete tutorial.
Methods