Mahfuzah Mustafa

EEG Analysis for Brainwave Balancing Index (BBI)

The purpose of this research is to establish the fundamental brainwave balancing index (BBI) using EEG signals. Brainwave signals from EEG were measured and analyzed using intelligent signal processing techniques and specific algorithm. Consequently, the signals were statistically correlated with established psychoanalysis techniques to produce BBI system. The result shows that the PSD analysis provides reliable BBI with 80% conformity. The fundamental findings (brainwave balancing index and brain dominance) from this research can be served as a simple indicator of one’s thinking leading to great opportunity for positive human potential development.

The Analysis of EEG Spectrogram Image for Brainwave Balancing Application Using ANN

The purpose of this paper is to analysis EEG spectrogram image using Artificial Neural Network (ANN) for brainwave balancing application. Time-frequency approach or spectrogram image processing technique is used to analyze EEG signals. The Gray Level Co-occurrence Matrix (GLCM) texture feature was extracted from spectrogram image and passed through Principal components analysis (PCA) to reduce the feature dimension. The experimental result shows that ANN was able to analysis EEG spectrogram images with an optimized model in training by varying neurons in the hidden layer, learning rate and momentum.

Classification of heart sounds using a multilayer feed-forward neural network

Cardiovascular diseases are among chronic diseases that seriously threaten human health. Medical experts sometimes listen to heart sounds through auscultation system, either from stethoscope or PCG (phonocardiogram), as one of the ways in diagnosing cardiovascular diseases. The auscultation process is subjective and largely depends on the experience, skill, knowledge or hearing ability of the physician to interpret heart conditions. This paper examines heart valve-related diseases using a multilayer feed-forward neural network (MFNN). The heart sounds were digitally filtered with moving average filter, segmented with sliding window and then reduced to an arbitrary frame of 64 points. After that, it is pre-processed by fast Fourier transform prior to feeding into the neural network for classification. To check for network robustness, the heart sound samples were injected with different level of noise. A total of 55 types of sample data were generated and used for classification. The study produces a 100% correct classification of eleven heart-valve diseases.

Initial experimental results of real-time variant pose face detection and tracking system

A face detection system is a computer application for automatically detecting a human face from digital image or video frame. This paper presents a face detection system that used web camera to detect and track a face in real-time. To detect a face in the image, a simple method of skin color detection is used. By using color detection method in this project, the face can be segmented easily from the complex background. However, to detect a face in real-time is quite challenging especially when a face is moving and the real-time environment has uneven illumination. This paper presents the preliminary result of face detection and tracking system, which is the system, detects a face that has different poses in a real-time situation, where the light condition is uneven. Here, to complete the detection process, contour detection method is added so that the detection is more accurate. This system can be applied in many applications such as banking system to reduce the number of forgery, security system, and human-computer interaction (HCI).

GLCM texture feature reduction for EEG spectrogram image using PCA

In Electroencephalography (EEG) research, the analysis using its time or frequency signals are very popular. However, it has been shown elsewhere, that any feature rich signals can be examined using time-frequency components. This paper proposes a new technique of extracting Gray-level Co-occurrence Matrices (GLCM) texture via time-frequency analysis of EEG signals. The output of this technique produces a big feature matrix and it is reduced by applying Principal Components Analysis (PCA). The results of this experiment shows that EEG signals can be analysed or described using five major components of the GLCM.

Forward Kinematics of 3 Degree of Freedom Delta Robot

Delta robot is a type of parallel robot. It consists of three arms connected to universal joints at the base. In this project, 3 degree of freedom Delta robot use of parallelograms in the arms, this maintains the orientation of the end effectors. The development of the Delta robot corresponds to the current requirement of having a robot able to transfer amount of light object in the least time. In this paper involves forward kinematics calculation using S-S (spherical-spherical) joint pair and compared with real position of Delta robot.

Interpretation of human thought using EEG signals and LabVIEW

This document describes the analysis of Electroenchaplogram (EEG) or brain signals using computational tool (LabVIEW) to interpret human thought such as moving forward, backward, turn right, turn left and to stop. This study is conducted to assist the disable people to communicate with external environment. The EEG signals are captured using wireless EEG amplifier while the subject in relax conditioin. Then, the signals are analyzed in LabVIEW to reveal the features to describe human thought. The features will be applied by the state machine to control the movements. The extracted EEG features are the ratio of EEG power spectrum from 14 channels. The Read Biosignal and STFT Spectrogram Toolbox of LabVIEW are used to process the EEG raw data and to produce the EEG Power Spectrum. Then, the pattern of the EEG Power Spectrum are studied to provide the feature vectors for the state machine. The outcome of the study indicates that the movement or direction can be determined based on the extracted features of EEG signals in LabVIEW.

Intelligent System for Assessing Human Stress Using EEG Signals and Psychoanalysis Tests

This paper presents a results of designing an intelligent system to evaluate human stress level using Electroencephalogram (EEG) signals and Psychoanalysis tests. The questionnaires for Psychoanalysis tests were created based on Cohens Perceived Stress Scale (PSS). EEG signals were captured using wireless EEG equipment. The Graphical User Interface (GUI) for the Psychoanalysis tests and EEG signals were created. The system was evaluated for 12 healthy subjects (7 females and 5 males). The results show that the intelligent system able to display the stress score, stress level and dominant index of EEG signals simultaneously. Thus, users can use the results of the system to take necessary action in order to improve their lifestyle.

GLCM texture classification for EEG spectrogram image

Over the past century, time based and frequency based is used for analyzing Electroencephalography (EEG) signals. EEG is a scientific tool for measure signal from human brain. This paper proposes a time-frequency approach or spectrogram image processing technique for analyzing EEG signals. Gray Level Co-occurrence Matrix (GLCM) texture feature were extracted from spectrogram image and then Principal components analysis (PCA) was employed to reduce the feature dimension. The purpose of this paper is to classify EEG spectrogram image using k-nearest neighbor algorithm (kNN) classifier. The result shows classification rate was 70.83% for EEG spectrogram image.

Computer vision-based hand deviation exercise for rehabilitation

Computerized monitoring of the home based rehabilitation exercise has many benefits and it has attracted considerable interest among the computer vision community. Nowadays, many rehabilitation systems are proposed, most of the targeted disability is for stroke patient. Some of patient or user just wants to take certain part for rehabilitation. Therefore, this paper is focusing on hand rehabilitation system. The importance of the rehabilitation system is to implement the specific exercise for the specific requirements of the patients that needs rehabilitation therapy. This paper presents the specific hand rehabilitation system using computer vision method. The specific hand rehabilitation implemented in this system is a hand deviation exercise. This exercise is benefited to improve the mobility of the hand and reduce the pain. The hand tracking and finger detection method are used in this hand rehabilitation system. The result of the exercise can be used as a training data for the analysis of the injured hand recovery and healing process.