identification of human emotions from EEG signals Using Deep Learning Model

Abstract:               Our project is based on Detecting Human emotions using Brain signals called Electroencephalogram Signals (EEG). Our focus is on the main aspects of the recognition phase that are Subjects, Features extraction and Clas

Project Title

identification of human emotions from EEG signals Using Deep Learning Model

Project Area of Specialization

Artificial Intelligence

Project Summary

Abstract:

              Our project is based on Detecting Human emotions using Brain signals called Electroencephalogram Signals (EEG). Our focus is on the main aspects of the recognition phase that are Subjects, Features extraction and Classifier. we will work on better accuracy of the results. we will be detecting Valance and Arousal class labels.

Project Objectives

Objectives:

• Exploring DEAP dataset
• What are the best features to extract from the eeg signals which can be mapped to specific emotional state 
• Improving accuracy and detecting different human emotional states.
• helping Medical field by identifying one's emotional state and help him getting out of his or her mental stress. 

Project Implementation Method

In our project, we are not extracting EEG signals Data directly from brain infact we are using proposed Dataset for it that is DEAP dataset for Emotion recognition 

DEAP dataset   -->   Preprocessing the Data   -->   Feature Extraction  -->   Applying Classifier  -->   The Required Output

Benefits of the Project

Self-learning:

• As a Student, it is benificial as we cover Some part of Brain Computer Interface BCI and we will learn Deep Learning Algorithms as well. after this we will be able to solve any problem related to the Classification. 
• we will do this project with Python. so it will help us with using ML libraries and how to use them.

Projec Scope:

• Another benifit is after recognizing the emotional state of a person, one can help them from mental-stress.
• Entertainment: EEG based music player, Games 
• Research; BCI, Deep Learning Models, Robotics.

Technical Details of Final Deliverable

Our project is to identify human emotion from Electroencephalogram(EEG) signals. Human emotions can also be identified through face and also from vocal But this identification can be faked. 
Human emotion identification from EEG signals is a step to step process. The very first step is to have access for dataset. Now, we can work on the available record i.e previous recorded dataset and can also record signals through brain which is a bit difficult process. So, in our project we opt to work on an existing dataset.  
After dataset selection we will clean our dataset from redundancy by applying techniques. 
When our dataset is precise and ready to work on it. We will divide our dataset in two portions the larger one is the data to be trained and the smallest one is the test data.
We will train our dataset by using deep learning classifier which will classify the emotions present in the dataset. 
The classifiers include are:
-KNN 
-Naive bayes
-decision tree
This is the detailed process obviously which can’t be explained in this limit. Before classifying we will go for feature extraction. In this way we will train our dataset and then we will test it for the separated portion of dataset which we call as test dataset.
In the end we will check the accuracy rate for our project our the classifiers which we applied on the dataset.

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

Others , Security

Core Technology

Artificial Intelligence(AI)

Other Technologies

Robotics, Big Data

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com