deap-dataset

This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.

EEG-based emotion classification using DEAP dataset

[BIBM2024] MEEG and AT-DGNN: Improving EEG Emotion Recognition with Music Introducing and Graph-based Learning

Scripts to a) download DEAP EEG dataset b) preprocess its EEG signals and c) perform feature extraction

Emotion Recogniton LSTM RNN Arousal Valence

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

Simply emotion analyse and classify using EEG data based on DEAP dataset, using python and sklearn(SVM,KNN,Tree). 简单的EEG脑电数据情感分析，使用python和DEAP数据集。

Emotional Video to Audio Transformation with ANFIS-DeepRNN (Vanilla RNN and LSTM-DeepRNN) [MPE 2020]

KNN classifier for human emotion from EEG

Affective EEG-Based Person Identification Using the Deep Learning Approach (IEEE Transactions on Cognitive and Developmental Systems)

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

Statistical comparison study of valence-arousal classifiers from EEG signals on DEAP and MANHOB datasets

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".

emotion recognition through eeg by using HOS method

Emotion recognition from EEG data (Bachelor's thesis), using the DEAP dataset. Performed manual feature selection across three domains: time, frequency, and time-frequency. Used different classifiers, including XGBoost, AdaBoost, Random Forest, k-NN, SVM, etc.

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

EEG classification project comparing neural network (MLP) with classical machine learning models using EEG bandpower features. Includes training pipeline, performance analysis, and visualizations.

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

This project classifies emotions using EEG signals from the DEAP dataset, achieving good performances with Machine Learning and Deep Learning Models

EEG signal processing and exploratory analysis using the DEAP dataset, focusing on Valence and Arousal emotion dimensions.