Please use this identifier to cite or link to this item:
|Title:||Brain signal analysis I||Authors:||Ang, Li Fang||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics||Issue Date:||2009||Abstract:||This report provides an overview of the Electroencephalogram (EEG) Brain Signal Analysis Project. The goal of the project is to study the use of EEG to map the areas of the brain stimulated by two emotional states – Happy and Sad. The first portion of the report offers the literature review of the brain signal and EEG. This includes the brain structure and emotion. It gives a better understanding of the neurophysiology and various functions in the different parts of the brain. In this project, the plutchik theory is adopted to represent the emotion. Each emotion is directly related to an adaptive pattern of behavior necessary for survival. There were twenty-three subjects (All males) who participated in the experiment. They were shown an audio-visual slideshow consists of a set of pictures (10 happy and 10 sad) from the International Affective Picture System (IAPS) embedded with sound clips. The EEG data collected from the DAQsys system passed through a Butterworth band pass filter. The result showed the peak of the power spectrum density (PSD) was observed in the range of 8Hz to 13Hz indicating alpha brain wave was extracted. Another observation was small signal was detected beyond 20Hz which pointed out power line noise was removed. Next this filtered data was placed into the EEGLab and used to estimate the dipole source “location” for the brain signal in the datasets. After which it stored the dipole “locations” as the template for Happy and Sad respectively. Based on the comparison with five subjects, the result shows the dipoles were found at the front portion of the brain which exhibited happy state stimulation in the subjects. On the other hand, the dipoles found at the back portion of the brain responded to sad state stimulation in the subjects. The final stage was to classify the data by comparing the smallest distance between testing data and the happy and sad templates. It shows an average of 60% of the data was classified correctly and its highest percentage of accuracy is 71.333%.||URI:||http://hdl.handle.net/10356/16696||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
checked on Sep 28, 2020
checked on Sep 28, 2020
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.