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neuraltrain.models.green.Green

pydantic model neuraltrain.models.green.Green

Reference: GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals. See https://arxiv.org/pdf/1808.00158.

Parameters:

• sfreq (int | None, default=None) – Sampling frequency of the signal.

• n_freqs (int, default=15) – Number of main frequencies in the wavelet family.

• kernel_width_s (int, default=5) – Width of the kernel in seconds for the wavelets.

• conv_stride (int, default=5) – Stride of the convolution operation for the wavelets.

• oct_min (float, default=0) – Minimum frequency of interest in octave.

• oct_max (float, default=5.5) – Maximum frequency of interest in octave.

• random_f_init (bool, default=False) – Whether to randomly initialize the frequency of interest.

• shrinkage_init (float, default=-3.0) – Initial shrinkage value before applying sigmoid function.

• logref (str, default='logeuclid') – Reference matrix used for LogEig layer.

• dropout (float, default=0.333) – Dropout rate for FC layers.

• hidden_dim (list[int], default=[32]) – Dimension of the hidden layer. If None, no hidden layer.

• pool_layer (str, default='RealCovariance') – Pooling layer type. Options: ‘RealCovariance’, ‘PW_PLV’, ‘CombinedPooling’, ‘CrossCovariance’, ‘CrossPW_PLV’, ‘WaveletConv’.

• bi_out (list[int] | None, default=None) – Dimension of the output layer after BiMap.

• use_age (bool, default=False) – Whether to include age in the model.

• orth_weights (bool, default=True) – Whether to use orthogonal weight initialization.

Fields:

• bi_out (list[int] | None)

• conv_stride (int)

• dropout (float)

• hidden_dim (list[int])

• kernel_width_s (int)

• logref (str)

• n_freqs (int)

• oct_max (float)

• oct_min (float)

• orth_weights (bool)

• pool_layer (Literal['RealCovariance', 'PW_PLV', 'CombinedPooling', 'CrossCovariance', 'CrossPW_PLV', 'WaveletConv'])

• random_f_init (bool)

• sfreq (int | None)

• shrinkage_init (float)

• use_age (bool)

field sfreq: int | None = None

field n_freqs: int = 15

field kernel_width_s: int = 5

field conv_stride: int = 5

field oct_min: float = 0

field oct_max: float = 5.5

field random_f_init: bool = False

field shrinkage_init: float = -3.0

field logref: str = 'logeuclid'

field dropout: float = 0.333

field hidden_dim: list[int] = [32]

field pool_layer: Literal['RealCovariance', 'PW_PLV', 'CombinedPooling', 'CrossCovariance', 'CrossPW_PLV', 'WaveletConv'] = 'RealCovariance'

field bi_out: list[int] | None = None

field use_age: bool = False

field orth_weights: bool = True

build(n_in_channels: int, n_outputs: int, sfreq: int | None = None) → Module

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