min2net.model.EEGNet

min2net.model.EEGNet

About EEGNet

Original authors have uploaded their code here https://github.com/vlawhern/arl-eegmodels

If you use the EEGNet model in your research, please cite the following paper:

@article{Lawhern2018,
  author={Vernon J Lawhern and Amelia J Solon and Nicholas R Waytowich and Stephen M Gordon and Chou P Hung and Brent J Lance},
  title={EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces},
  journal={Journal of Neural Engineering},
  volume={15},
  number={5},
  pages={056013},
  url={http://stacks.iop.org/1741-2552/15/i=5/a=056013},
  year={2018}
}

EEGNet class

Configures the model for training. Based on tf.keras.Model.

min2net.model.EEGNet()

Arguments:

Arguments	Description	Default
input_shape	`tuple` of integers. (1, #channel, #time_point)	(1,20,400)
num_class	`int` number of class.	2
loss	`str` (name of objective function), objective function or tf.keras.losses.Loss instance.	`'sparse_categorical_crossentropy'`
epochs	`int` number of epochs to train the model.	200
batch_size	`int` or `None`. Number of samples per gradient update.	100
optimizer	`str` (name of optimizer) or `optimizer instance`. See tf.keras.optimizers.	Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-08)
lr	`float` the start learning rate	0.01
min_lr	`float` lower bound on the learning rate. See tf.keras.callbacks.ReduceLROnPlateau.	0.01
factor	`float` factor by which the learning rate will be reduced. See tf.keras.callbacks.ReduceLROnPlateau.	0.25
patience	`int` number of epochs with no improvement after which learning rate will be reduced. See tf.keras.callbacks.ReduceLROnPlateau.	10
es_patience	`int` number of epochs with no improvement after which training will be stopped. See tf.keras.callbacks.EarlyStopping.	20
verbose	`0` or `1`. Verbosity mode. 0 = silent, 1 = progress bar.	1
log_path	`str` path to save model	‘logs’
model_name	`str` prefix to save model	‘EEGNet’
**kwargs	Keyword argument to pass into the function for replacing the variables of the model such as `kernLength`, `F1`, `D`, `F2`, `norm_rate`, `dropout_rate`, `f1_average`, `data_format`, `shuffle`, `metrics`, `monitor`, `mode`, `save_best_only`, `save_weight_only`, `seed`, and `class_balancing`	-

Build method

Build the model that group layers into an object with training and inference features.

EEGNet.build()

Returns: Model (tf.keras.Model): Model object

Fit method

Fit the model according to the given training and validation data. This method was implemented based on tf.keras.Model.fit(). The model weights and logs will save at 'log_path'.

EEGNet.fit(X_train, 
           y_train, 
           X_val, 
           y_val)

Arguments:

Arguments	Description
X_train	`ndarray` Training EEG signals. shape (#trial, #depth, #channel, #time_point)
y_train	`ndarray` Label of training set. shape (#trial)
X_val	`ndarray` Validation EEG signals. shape (#trial, #depth, #channel, #time_point)
y_val	`ndarray` Label of validation set. shape (#trial)

Predict method

Generates output predictions & the loss value & metrics values for the model in test mode for the input samples. This medthod was implemented based on tf.keras.Model.predict() and tf.keras.Model.evaluate().

EEGNet.predict(X_test, 
               y_test)

Arguments	Description
X_test	`ndarray` Testing EEG signals. shape (#trial, #depth, #channel, #time_point)
y_test	`ndarray` Label of test set. shape (#trial)

Returns:

Y: dictionary of {y_true, y_pred}
evaluation: dictionary of {loss, accuracy, f1_score}

Example

from min2net.model import EEGNet
import ndarray as np

model = EEGNet(input_shape=(1,20,400), num_class=2, dropout_rate=0.25, shuffle=True)
model.fit(X_train, y_train, X_val, y_val)

Y, evaluation = model.predict(X_test, y_test)