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

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

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

[DEAP] Attention-Based Temporal Learner With Dynamical Graph Neural Network for EEG Emotion Recognition.

Emotion Recogniton LSTM RNN Arousal Valence

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)

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

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.

Master MVA - Time Series Project

Final project for the master's degree in Computer Science course "Multimodal Interaction" at the University of Rome "La Sapienza" (A.Y. 2023-2024).