Haryanti Norhazman

Support vector machine for classification of stress subjects using EEG signals

Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women. SVM is used to classify the EEG Alpha band data for Power Spectral Density and Energy Spectral Density. Using 5-fold cross validation, the classification rate are 83.33% for ESD data using RBF kernel function.

The investigation of alpha frontal energy asymmetry on normal and stress subjects after listening to the binaural beats 10 Hz

In this modern era, life is becoming more challenging and faster paced, where we are highly demanded with the complex needs of our daily life that requires more focus and efforts. Such daily needs will eventually lead us to stress if not handled properly. These negatives effects if go undiagnosed, can be fatal. However, it can be turned into positive effects if we know how to manage it. One way of stress management is through meditation. However, to some people, this act is hard to perform because their brains refuse to obey to be silence and focus. Thus, one way for them to get into the mood easier and quicker is through brainwave entrainment; binaural beat specifically. Our study focuses on the effect of the binaural beats, 10Hz on the fontal alpha energy asymmetry of normal and stress subjects. The total numbers of subjects involved are 42 which comprises of 18 subjects in normal group and 24 subjects in stress group. The EEG recording is done in two sessions that are before and after listening to the binaural beats. Normal subjects experience an increment of 90.1% in their frontal alpha asymmetry while stress subjects experience only 1.37%. The results suggested that frontal alpha energy asymmetry could be an indicator to show the positive effects of the binaural beats sound to human brainwave.

Dynamic encoding of binaural beats for brainwave entrainment

This paper shows the development of the dynamic binaural beat encoding using JAVA program. Binaural beat is an auditory phenomenon that has been used worldwide to alter our brain state, to get to the desired state, e.g. Relax. However, most of the Binaural Beat system designed is a static binaural beat on which the user is not able to alter the frequency of the audio. The binaural beat frequency listened by the user might not be suitable to the users current brain state. Such incompatibility may cause the user to feel uncomfortable and may also cause dizziness while listening to the audio being played. Hence, dynamic encoding of binaural beats is designed and implemented to replace the existing static encoding of binaural beats. The GUI of the system has two options, Manual Configuration and Automatic Configuration on which the user able to choose the base frequency and the offset frequency as desired. The developed system has been tested and validated in the lab to show its ability to generate the binaural beat frequency.

Depression Diagnostic and Screening Tools Using Android OS Platform

Depression affects all walks of life and is a common form of mental health illness. Some of the common methods to diagnose depression are usually through a session with certified psychiatrist or with the aid of depression rating scales. This paper seeks to provide both clinicians and patients with an Android based mobile application that may store and calculate results based on depression rating scales i.e. DASS and MINI. A novel approach of combining both questionnaire statistics is proposed in one solution. As such, a tablet friendly application that uses a scoring algorithm and a series of psychiatric questionnaires as an indicator to a person’s mental state or depression level is developed by means of an android platform.