seed-dataset

Emotion Recognition using ML methods on the SEED dataset

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

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".

Hierarchical Dynamic Local-Global-Graph Representation Learning

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

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

GraphCNN + CNN Network for EEG Emotion Recognition

LDA(Linear Discriminant Analysis) for Seed Dataset

Kmeans and Hierarchical clustering for Seed-dataset in Machine Learning

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

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

This is a collection of synthetically generated datapoints for fine tuning meant for seeding larger synthetic datasets.

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

Engineering Practice and Scientific and Technological Innovation Homework IV

PyTorch implementation of Temporal-Spatial Graph-Integrated Framework for EEG-Based Emotion Recognition (SEED Dataset)

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

Interpretable EEG-based emotion recognition using EEGNet and Captum (Integrated Gradients), with brain topographic mapping via MNE-Python on the SEED dataset.