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aidl-eeg

This project seeks to apply deep learning techniques to electroencephalography (EEG) data collected in the context of subject emotion recognition.

amigos-emotion-classification

annotated_deep_learning_paper_implementations

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

athena

Athena is a library that comprises many different bci frameworks that perform classification on a set of eeg data.

awesome-attention-mechanism-in-cv

:punch: CV中常用注意力模块;即插即用模块;ViT模型. PyTorch Implementation Collection of Attention Module and Plug&Play Module

bilstm-crf

developed with tensorflow 2.1.0

block-recurrent-transformer

Pytorch implementation of "Block Recurrent Transformers" (Hutchins & Schlag et al., 2022)

chinese_bilstm_cnn_crf

keras+tensorflow+python3下的中文分词, 大数据可训练，解决内存不够用问题

crf

keras implementation of conditional random field

crossculturecode

deap-cnn-lstm

Emotion recognition based on DEAP dataset using One-Dimensional CNN, dan RNN (GRU, and LSTM).

deep-learning-based-emotion-recognition

Emotion Recognition, EEG Mapping, Azimuthal Projection Technique, CNN

ecnn-c

Code for paper: EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning

eeg-transformer

i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

eeg_emg

some dataset and algorithm on EEG-EMG fusion processing

emotion-recognition-from-brain-eeg-signals-

Emotion recognition can be achieved by obtaining signals from the brain by EEG . This test records the activity of the brain in form of waves. We have used DEAP dataset on which we are classifying the emotion as valance, likeness/dislike, arousal, dominance. We have used LSTM and CNN classifier which gives 88.60 % accuracy to predict the model successfully.

ewtpy

Empirical wavelet transform (EWT) in Python

external-attention-pytorch

🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

keras-crf

A more elegant and convenient CRF built on tensorflow-addons.

lgg

This is the PyTorch implementation of LGGNet.

multi-task-cnn-eeg-emotion

Code for the paper "Multi-Task CNN Model for Emotion Recognition from EEG Brain Maps". DEAP dataset. Python/Keras/Tensorflow 2 Impementation.

recs

Real-time Emotion Recognition using Physiological signals in e-Learning Here one can find the development of realtime emotion recognition using various physiological signals

self-supervised-eeg-classify

Tensorflow implementation for "A Novel Solution for EEG-based Emotion Recognition"

sfcsan

Code for "Spatial-Frequency Convolutional Self-Attention Network for EEG Emotion Recognition"

signal-feature-extraction_de-and-psd

Code for extracting DE (differential entropy) and PSD (power spectral density) feature of signals.

sst-emotion-net-pytorch-version-

sst-emotionnet

SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition

tf2crf

CRF layer for tensorflow 2 keras

uform

Multi-Modal Inference Library For Semantic Search Applications and Mid-Fusion Vision-Language Transformers

vit-tensorflow

Vision Transformer Cookbook with Tensorflow