Emotion Recognition using ML methods on the SEED dataset

Kmeans and Hierarchical clustering for Seed-dataset in Machine Learning

PCA(Principle Component Analysis) For Seed Dataset in Machine Learning

LDA(Linear Discriminant Analysis) for Seed Dataset

Engineering Practice and Scientific and Technological Innovation Homework IV

This repo is the result of a project assignment for a machine learning course at my university which was assisted by other group members. This project is to create a website that can cluster from the models that have been made. This model was created using the KMeans algorithm with 3 clusters that were trained with the seed dataset

GraphCNN + CNN Network for EEG Emotion Recognition

Detecting Concept Shifts under Different Levels of Self-awareness on Emotion Labeling

Cascading global and sequential temporal representations with local context modeling for EEG-based emotion recognition

Official implementation of AWRP encoding for EEG-based emotion recognition, as proposed in our IEEE Access 2024 paper.

Convolutional Channel Modulator for Transformer and LSTM Networks in EEG-based Emotion Recognition

Hierarchical Dynamic Local-Global-Graph Representation Learning

EEG-based emotion recognition with convolutional neural networks and hyperparameter optimization on the DEAP and SEED benchmarks.

Official PyTorch implementation of RBTransformer from our paper "A Brain Wave Encodes a Thousand Tokens: Modeling Inter-Cortical Neural Interactions for Effective EEG-based Emotion Recognition".