1. Major Project Presentation Phase - I
R.V.COLLEGE OF ENGINEERING,BENGALURU

2. R. V. COLLEGE OF ENGINEERING BENGALURU – 560059
The Major Project Phase – I Presentation on
“Emotion Recognition using EEG Signals”
Presented by
BABASAB GADADE (1RV16SSE03)
Under the Guidance of
Dr. N. K. CUAVERY
Head of the Department
Department of ISE, RVCE, Bengaluru

3. CONTENTS Introduction Problem Statement Objectives Motivation
Literature Survey
Methodology
Expected Output
Progress Status
Conclusion
References

4. INTRODUCTION EEG signals
Electroencephalography is a non-invasive neuroimaging technique that records the electrical activity of the brain.
EEG measures voltage fluctuations resulting from ionic current within the neurons of the brain. The voltage fluctuations are called “Brain Waves”.
Different types of Brainwaves:
Theta waves (3-8Hz)
Alpha waves (8-12Hz)
Beta waves (12-38Hz)
Gamma waves (38-42Hz)
Emotion Recognition

5. INTRODUCTION (Ctnd..)
The EEG differs from the other neuroimaging techniques due to its high temporal resolution.
The EEG is often combined with
magnetic resonance imaging (MRI)
computed tomography (CT)

6. PROBLEM STATEMENT
Recognise the Emotion which is the conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure.
Identification of complex natured Emotions that involve different components.
Brain – Computer Interface Systems to recognise the emotions – subjective experience.
Feelings - a subjective representation of emotions.
Subjective Experiment on different subjects
Analyse different feelings on different subjects with various trials

7. OBJECTIVES
Testing the complete BCI application on a user in real time.
Accurate collection of huge, noise free training dataset.
Understand the Machine – Learning Domain.
Choose the best and efficient classification algorithm.
Making the emotion recognition system convenient to use.
Obtain the best accuracy outcomes.

8. MOTIVATION Brain Computer Interface (BCI)
Basic electrophysiological research
Medical application development - Clinical applications include sleep monitoring and epilepsy
Neuromodulation - EEG neuromodulation is the process of modifying brain state via feedbackbased training.
Biometry - biometry platform to authenticate users based on EEG signatures.
User affective state - User emotional state can be derived from physiological signals(EEG) provides a platform for research and development in affective computing and smart systems.

9. LITERATURE SURVEY

10. LITERATURE SURVEY (Ctnd..)

11. METHODOLOGY

12. METHODOLOGY (Ctnd..) Noise Reduction
The background EEG noise is µV which is much higher than the Enobio/Starstim amplifier noise. Device electronic noise adds only a few percent to the total noise level.
The devices does not apply a line noise filter. Instead, the NIC software optionally uses a real-time 50/60 Hz line noise cancellation algorithm that conserves the original signal. The filter can optionally be applied by the user to the recorded data.
Measurement of signals -125Hz
Sampling Rate -500 SPS

13. EXPECTED OUTPUT
Emotions categories are identified with a proper accuracy
Four different emotions recognition
Arousal, Valence, Liking and Dominance
The classifier accuracy %

14. PROGRESS STATUS
Enobio is a wireless and portable electrophysiology sensor system for the recording of the electroencephalogram (EEG).
Installation of NIC
Working on Enobio - 8 EEG Headset.
TCP/IP connection established for testing the EEG data in real time.
Successfully planned to test on 40 different subjects with 40 trials each.

15. CONCLUSION
Emotion Recognition using EEG signals, outcomes in four different emotions with an accuracy of 55.7%.
The different emotions recognised are:
Arousal
Valence
Liking
Dominance

16. REFERENCES