Modeling Human Emotion during watching movies using EEG Prepared By : Muniratul Husna Bt. Mohamad Sokri Matric No. : Lecturer : Dr. Farzana binti Kabir Ahmad

Outline » Introduction » Problem Statement » Significant of study » Research questions & Research Objectives » Literature Review » Methodology

Introduction

Why used EEG in detecting human emotion? Emotion is a psycho physiological process that directly related to brain activities. So, the electrical activities of the brain can be measure by EEG. The recent research had proven that using EEG signals can recognize emotion more than 60% accuracy.

Problem Statement It is not sure EEG Asymmetry correlate with emotions can be show using different elicitation procedures There is no confirmation about predominant in EEG Asymmetry correlate with emotions. The correlations between brain regions and various emotions are not consistent. The interpretation of emotions affective information is still lacking and human emotions can’t be defined by human-computer interactions. The classification results of EEG are not reliable. The recent technology using EEG are not good enough for emotion recognition.These signals not fit for people who want to conceal their feelings.The facial expression and prosody are not reliable to detect emotion.

Significant of Study Artificial Intelligent Area Robots that can communicate will people Patient monitoring systems Various Intelligent System Paralyse patients Monitoring Systems

Research Question & Research Objectives RQ: Can we possible identify the connection between human emotions an EEG signals? RO : To identify the connection between human emotions and EEG signals. RQ: Which part of brain that is likely associated to emotions? RO : To identify the brain regions and frequency bands that is likely associated to emotions. RQ: How to classify EEG signals into different classes of emotions by watching movies? RO : To establish the model that classifies EEG signals into different classes of emotions by watching movies.

Literature Review N o. AuthorYearResultsBrain Regions and Frequency BandsModels 1B. Danny200870% 1.Temporal lobe and frontal cortex. 2.Beta and alpha frequency. Binary linear FDA (Fisher’s Discriminant Analysis) classifiers are used. 2 M. Soleymani, M. Pantic, T. Pun % (valence) 55.7% (arousal) 1.Theta, alpha, and gamma frequency. Power Spectral Density (PSD) 3 D. Nie, X.W. Wang, L.C. Shi, B.L. Lu % 1.Beta, alpha, and gamma frequency. 2.Occipital Lobe,central site,parietal lobe, frontal and right temporal lobe. Power Spectral Density (PSD) 4 K. Eleni, Y. Ashkan, E. Touradj % 1.Left and right frontal brain. 2.Theta, alpha, beta and gamma frequency. Fisher’s method. 5M. Murugappan et al %Delta, theta, alpha, beta and gammaEntropy

Methodology Phase 1 : Data Collection Phase 2 : Methods Identification Phase 3 : Model Evaluation

Methodology PhaseMethodsOutput Phase 1 : Data Collection 1.Do experiment on correspond participants. 2.Tools 3.Stimuli Phase 2 : Methods Identification 1.Feature Extraction Power Spectral Density (PSD) 2. Classification Support Vector Machine (SVM) 3. Feature Smoothing Linear Dynamic System (LDS) 1.Connection between EEG Signals and human emotions. 2. Identification of the brain regions and frequency bands. Phase 3 : Model Evaluation 1.Classification Cognition theory (two-dimensional scale ) 3. Model of emotions representation.