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"model_name": "EEG Data Synthesis with WGAN-GP",
"architecture": "conditional_wgan_gp",
"latent_dim": 128,
"num_subjects": 109,
"num_channels": 64,
"segment_length": 480,
"generator_fc_layers": [256, 2048, 7680],
"deconv_layers": [
{"type": "ConvTranspose1d", "kernel_size": 4, "stride": 4, "dilation": 1},
{"type": "Conv1d", "kernel_size": 3, "padding": 2, "dilation": 2},
{"type": "Conv1d", "kernel_size": 3, "padding": 4, "dilation": 4}
],
"activation": "tanh",
"optimizer": {
"type": "Adam",
"beta1": 0.0,
"beta2": 0.9,
"lr_generator": 1e-4,
"lr_discriminator": 5e-5
},
"training": {
"epochs": 300,
"batch_size": 42,
"gradient_penalty_lambda": 5,
"drift_regularization": 0.001,
"n_critic": 1,
"mixed_precision": true
},
"dataset": {
"name": "PhysioNet EEG Motor Movement/Imagery",
"num_subjects": 109,
"sampling_rate": 160,
"channels": 64,
"segment_length": 480,
"normalization": "[-1, 1]",
"tasks": ["left_fist", "right_fist", "both_fists", "both_feet", "eyes_open", "eyes_closed"]
},
"description": "Trained conditional EEG generator (WGAN-GP) using 109 subjects from the PhysioNet EEG Motor Movement/Imagery dataset."
}
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