GithubHelp home page GithubHelp logo

emotion_recognition's Introduction

Emotion Recognition from EEG Signals

Overview

This repository is intended to keep track of the project related to the Natural Interaction and Models of Affective and Behavioral Computing courses for the Computer Science Master at Università degli Studi di Milano.

Info

This project concerns emotion recognition from EEG signals dealt with by using a SVM classifier based on libsvm library, relying on the method described in the article.

This method consists in making classification and prediction using features extracted from decomposed EEG signals.

First of all, EEG signals taken from the DEAP dataset are decomposed into IMFs (Intrinsic Mode Functions) using the EMD (Empirical Mode Decomposition), then the first difference of time series, the first difference of phase and the normalized energy are extracted as features from the first IMF, the most informative one. Two disjoint sets are formed using the extracted features: a training set and a test set. Classification was made on the training set and the respective label set; then, prediction was made on the test set, to test the trained model on a different set without giving it the labels as input. Predicted values was confronted with the labels to check if the model prediction was right or wrong. Using cross-validation, it was possible to extimate the model accuracy for a single participant in the DEAP dataset. Finally the method accuracy was computed as the mean of the accuracies computed on the single participants.

For more info, read the report inside the repository.

Links

Article: https://www.hindawi.com/journals/bmri/2017/8317357

emotion_recognition's People

Contributors

alessioquercia avatar

Stargazers

 avatar Guantian Zheng avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.