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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.

FBMSNet

FBMSNet, a novel multiscale temporal convolutional neural network for MI decoding tasks, employs Mixed Conv to extract multiscale temporal features which enhance the intra-class compactness and improve the inter-class separability with the joint supervision of the center loss andcenter loss.

MTCNN

Multi-Task Convolutional Neural Network (MT-CNN).

STNet

Spatio-temporal Network (STNet).

TSCeption

TSCeption.

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.

ATCNet

ATCNet: An attention-based temporal convolutional network forEEG-based motor imagery classification.For more details ,please refer to the following information:

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.

EEGfuseNet

EEGFuseNet: A hybrid unsupervised network which can fuse high-dimensional EEG to obtain deep feature characterization and generate similar signals.

EFDiscriminator

EFDiscriminator: the discriminator that comes with EEGFuseNet is to distinguish whether the input EEG signals is a fake one generated by the eegfusenet or a real one collected from human brain.

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

This class implements the normalized flow model, allowing to generate samples close to the true distribution.

BCGlow

This class implements a conditional normalized flow model that allows generating samples of specified classes.

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.

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