Brain computer interface for the analysis of EEG signals using machine learning

Summary A brain?computer interface (BCI) is a direct communication pathway between brain and an external device. The idea behind the project tittle is to analyze EEG waves by using EEG sensors and apply m

Project Title

Project Area of Specialization

Artificial Intelligence

Project Summary

Summary

A brain–computer interface (BCI) is a direct communication pathway between brain and an external device. The idea behind the project tittle is to analyze EEG waves by using EEG sensors and apply machine learning on those signals to mine extra information. This project deals with development and as well as research. We aim to generate EEG data and map with some events such as stress, lie and deception, Machine learning will be applied to generated data either to detect stress level, deception, and level of focus.

Lie detector.

Lie or deception detection techniques are those in which questioning, and technology collaborates to record physiological function to ascertain truth and false responses. To make the Lie detector, EEG sensors will be used to detect brain waves in real time, along with the ECG sensor which detects heart rate and GSR sensors which detects skin resistance, for detecting deception. Machine learning will be applied to data generated via above mentioned sensors.

This system may allow us to detect changes in brain waves, skin sensations and heart rate by using EEG, ECG and GSR sensors, respectively. Supervised machine learning will be used to identify pattern and recognize and classify that a person is lying or telling truth. With the help of these sensors good accuracy rates may be achieved.

Stress detector

According to world health organization, stress is a significant problem of our times and affects both physical as well as the mental health of people. To make the stress detection and reduction device, the EEG sensors will be used to detect brain waves in real time, monitoring subconscious brain waves and the supervised machine learning will be implemented to data generated via EEG sensor.

The system will provide a real time monitoring of sub conscious state of mind which then helps the individual to control stress by using different technique and to monitor the changes in brain waves. This ultimately makes one aware of existing stress and to control it.

Project Objectives

Objective

The main objective and scope of this project is to analyze EEG waves to possibly detect the stress level or deception using machine Learning.

Project Implementation Method

Project implementation method

Lie detector

The lie detector will use the supervised machine learning to identify pattern and recognize and classify that a person is lying or not. This will be achieved by collecting EEG data using EEG sensors along with skin resistance sensor and heart rate sensors.

Series of questions will be asked from subject. While answering EEG sensor will detect the brain waves, on same time ECG sensor and galvanic skin sensors detects changes in heart rate and skin resistance, respectively. Based on these changes, machine learning algorithm will classify the answer of subject either a truth or a lie.

Stress detector

The stress detector will use the machine learning to detect the stress. This will be achieved by collecting EEG data.

Biofeedback can be achieved by EEG sensors to detect the stress level. The system will provide a real time monitoring of sub conscious state of mind which then helps the individual to control stress by using different technique and to monitor the changes in brain waves. This ultimately makes one enable to, awareness of existing stress and control it.

Benefits of the Project

Benefits of project

Lie detectors can be used to check the validity of testimony and criminal interrogation in following areas:
1. Courts
2. police station
3. FBI (federal bureau of investigation)
4. NAB (national accountability bureau)
5. Cybercrime.
Focus and stress detector can be used in smart classroom of handicap children to monitor their focus and stress during the lecture.

Technical Details of Final Deliverable

Technical details

Lie detector

EEG sensor will be attached to the subject’s frontal and the temporal areas on scalp to detect the brain waves in real time, ECG sensor will be attached to the subject chest and galvanic skin resistor sensor will be attached on the two fingers on a hand. Series of questions will be asked. During this process the signals generated by the sensors will be detected by the computer program after analyzing these signals the computer program will detect if the person is telling lie or truth.

Stress detector

EEG sensor will be attached to the subject’s scalp to detect the brain waves in real time. The EEG signals will be detected by the computer program after analyzing these signals the computer program will help identify the level of stress and allow subject to control it by some means of stress control techniques.