torcheeg.models

Convolutional Neural Networks

EEGNet	A compact convolutional neural network (EEGNet).
FBCCNN	Frequency Band Correlation Convolutional Neural Network (FBCCNN).
FBCNet	An Efficient Multi-view Convolutional Neural Network for Brain-Computer Interface.
MTCNN	Multi-Task Convolutional Neural Network (MT-CNN).
STNet	Spatio-temporal Network (STNet).
TSCeption	Continuous Convolutional Neural Network (CCNN).
CCNN	Continuous Convolutional Neural Network (CCNN).
SSTEmotionNet	Spatial-Spectral-Temporal based Attention 3D Dense Network (SST-EmotionNet) for EEG emotion recognition.

Recurrent Neural Networks

GRU	A simple but effective gate recurrent unit (GRU) network structure from the book of Zhang et al. For more details, please refer to the following information.
LSTM	A simple but effective long-short term memory (LSTM) network structure from the book of Zhang et al. For more details, please refer to the following information.

Graph Neural Networks

DGCNN	Dynamical Graph Convolutional Neural Networks (DGCNN).
LGGNet	DLocal-Global-Graph Networks (LGGNet).
pyg.RGNN	Regularized Graph Neural Networks (RGNN).
pyg.GIN	A simple but effective graph isomorphism network (GIN) structure from the book of Zhang et al. For more details, please refer to the following information.

Transformer

SimpleViT	A Simple and Effective Vision Transformer (SimpleViT).
ArjunViT	Arjun et al. employ a variation of the Transformer, the Vision Transformer to process EEG signals for emotion recognition.
VanillaTransformer	A vanilla version of the transformer adapted on EEG analysis.
ViT	The Vision Transformer.

Generative Adversarial Network

BGenerator	TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.
BDiscriminator	TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.
BCGenerator	GAN-based methods formulate a zero-sum game between the generator and the discriminator.
BCDiscriminator	GAN-based methods formulate a zero-sum game between the generator and the discriminator.

Variational Auto Encoder

BEncoder	The variational autoencoder consists of two parts, an encoder, and a decoder.
BDecoder	The variational autoencoder consists of two parts, an encoder, and a decoder.
BCEncoder	TorchEEG provides an EEG feature encoder based on CNN architecture and CVAE for generating EEG grid representations of different frequency bands based on a given class label.
BCDecoder	TorchEEG provides an EEG feature decoder based on CNN architecture and CVAE for generating EEG grid representations of different frequency bands based on a given class label.

Normalization Flow

BGlow

A flow-based model is dedicated to train an encoder that encodes the input as a hidden variable and makes the hidden variable obey the standard normal distribution.

Diffusion Models

BUNet	The diffusion model consists of two processes, the forward process, and the backward process.
BCUNet	The diffusion model consists of two processes, the forward process, and the backward process.