这是一个完整的，端到端的机器学习项目，非常适合有一定基础后拿来练习，以提高对完整机器学习项目的认识
Emotion Recognition from EEG Signals using the DEAP dataset with 86.4% accuracy. Applied multiple machine learning models and implemented various signal transforming algorithms like the DWT algorithm.
This repository contains the tensorflow implementation for the paper: "Emotion Recognition from Multi-Channel EEG through Parallel Convolutional Recurrent Neural Network"
This repository contains the tensorflow implementation for our ICONIP-2018 paper: "Continuous Convolutional Neural Network with 3D Input for EEG-Based Emotion Recognition" (To appear...)
Processed the DEAP dataset on basis of 1) PSD (power spectral density) and 2)DWT(discrete wavelet transform) features . Classifies the EEG ratings based on Arousl and Valence(high /Low)
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.
