Department of Biomedical Engineering (BME)

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Clench Strength Prediction for Prosthesis Hand Using Surface Electromyogram
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2012-09) Mostafa, Sheikh Shanawaz; Ahmad, Prof. Dr. Mohiuddin
Hand is the main environmental manipulator for humans. Therefore, any accidental lost of hand does a great harm in amputee life. Only choice left that he or she is using prosthesis, which is dated back to ancient time. Due to their usefulness prosthesis is also used in modern days. Now-a-day robotic rehabilitation opens a new era in the field of prosthesis. They are more capable of doing things compared to their ancestors. However, to duplicate the complex work of hand, the robotic hand is needed to complete two basic operations first one is predict the angular displacement and another one is the estimation of force. Estimation of force is essential for instance when drinking from a glass, one needs to apply sufficient grip force to prevent the object from slipping Out of the hand. In addition, one needs to control the total torque exerted on the glass such that the glass remains vertical. Usually, the requirements for grip force stabilization allow for some laxity, while the requirements for total torque production are highly specified. The grip force needs only to be larger than the slip threshold and smaller than the force that would break the object. Different researchers used different approach to solve this problem like body-powered prostheses, force-sensitive resistors. Nevertheless, when it comes to rehabilitation of amputee they are not up to the mark in practical field.Using of Electromyography (EMG) in the robotic rehabilitation shows some hope. Muscle tissue conducts electrical potentials similar to the way nerves do and the name given to these electrical signals is the muscle action potential. A muscle is composed of bundles of specialized cells capable of contraction and relaxation. The primary function of these specialized cells is to generate forces, movements and the ability to communicate such as speech or writing or other modes of expression. The skeletal muscle tissue is attached to the bone and its contraction is responsible for supporting and moving the skeleton. The contraction of skeletal muscle is initiated by impulses in the neurons to the muscle and is usually under voluntary control. Skeletal muscle fibers are well-supplied with neurons for its contraction. This depolarization, accompanied by a movement of ions, generates an electric field near each muscle fiber. An EMG signal is the train of Motor Unit Action Potential (MUAP) showing the muscle response to neural stimulation. There is a clear relationship between force and EMG. The higher the muscle force, the higher EMG level is developed. Clench force estimation is highly desirable in the field of prosthesis hand. It is one of the most used postures among different types of postures. In this thesis, the author proposes to estimate the clench force using two types of Surface Electromyography (SEMG): rectified SEMG and integrated SEMG. An Artificial Neural Network (ANN) is used to estimate the force from SEMG. For weight adjustment of the estimator Levenberg-Marquardt (L-M) back propagation algorithm is used. The proposed network is trained and tested using SEMG recorded from five subjects. The estimation result clearly shows that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross correlation coefficient. So integrated SEMG is recommended for clench force estimation. A neural number based experiment is also done to find the optimal number of neurons in hidden layer, which are five neurons as previously described neural network. In addition to that, these result also compared with Support Vector Machine (S\TM). Same result is found for integrated SEMG. In the case of rectified SEMG ANN is more suitable than SVM.
Improvement of Medical Imaging Equipment Management System of Bangladesh
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2012-06) Hossain, Md. Anwar; Ahmad, Prof. Dr. Mohiuddin
This thesis deals with the development of a standard Medical Imaging Equipment Management System (MIEMS) in the public hospitals of Bangladesh. Medical imaging equipment management has become an important component of health services delivery in Bangladesh. One of the most serious issues of Hospitals, Medical Center, Clinics and the health Systems is the threat in diagnosing diseases due to improper medical imaging equipment management system in Bangladesh. Presently a proper equipment management set-up does not exist in public hospitals and the overall diagnostic equipment condition in various departments is very poor. To find the facts and reasons the author visited fifteen medical imaging equipment departments in public hospitals of Bangladesh. From the data it is seen that a big percentage of the required management steps were found to be lacking. Based on the findings of the studies and with a view to improving the existing MIEMS, it is proposed to improve the medical imaging equipment management system for public hospitals in Bangladesh. The major findings are insufficient Biomedical Engineering Team, negligible in-house and central MIEMS, weak bridge between users and biomedical engineering team. The Ministry of Health and Family welfare of Bangladesh Govt., maintains a separate Medical Imaging Equipment Management System, which is not consistent with this plan. The MIEMS is designed to assure selection of appropriate medical imaging equipment to support the medical care processes of the nation and its ambulatory care facilities. The program is designed to assure effective preparation of staff responsible for the use or maintenance of the equipment. Finally, the program is designed to assure continuous availability of safe, effective equipment through a planned maintenance program, timely repair and evaluation of all events that could have an adverse impact on safety of patients or staff. The Clinical Biomedical Engineering Department staff has primary responsibility to maintain and repair all medical equipment including equipment under contract maintenance agreement by Vendors. All staff who uses medical equipment is required to learn and implement various general and specific procedures to ensure safe and reliable use of medical equipment. In addition, it will provide a proper guideline for all patient care staff, failures and user errors associated with the use of medical equipment. Regular maintenance and evaluation are necessary to assure that particular equipment delivers the expected performance within specified para
Modeling and Classification of Voluntary and Imagery Movements through fNIR & EEG Signals for Brain-Computer Interface
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh, 2020-01) Rahman, Md. Asadur; Ahmad, Prof. Dr. Mohiuddin
Neural activation measurement regarding voluntary and imagery movement is a crucial argument for brain-computer interface. Generally, movement-related hemodynamics is measured from the central lobe of the brain which may be erroneous for the paralyzed people due to their inactiveness of this brain area. To overcome this limitation, this thesis work proposes an approach to measure the movement-related hemodynamics from the prefrontal cortex. In the proposed research work, the changes of the oxidized hemoglobin (HbO2) and deoxidized hemoglobin (dHb) concentration regarding different voluntary and imagery movement stimuli are captured by functional near-infrared spectroscopy (fNIR) from several subjects. With necessary preprocessing, the fNIR signals are statistically analyzed by ANOVA and effect size method to localize the most significantly activated regions of the prefrontal cortex regarding the voluntary and imagery stimuli. The experimental results show that voluntary and imagery movements have a strong correlation with the prefrontal cortex. The temporal pattern of HbO2 and dHb signals regarding the most activated regions are modeled by polynomial regression. Consequently, the model activation patterns are used to classify the voluntary and imagery tasks based on the maximum similarity approach. In addition, conventional classification methods are used to classify the signals. In this work, we consider two, four, and six class fNIR data of movement-related tasks for classification. The classification accuracies of the proposed method are convincing and found almost similar to the conventional procedure. The outcomes of this proposed work suggest that the prefrontal hemodynamics can be used for the modeling and classification of the voluntary and imagery movement-related tasks which will be helpful for the brain-computer interface applications. The combination of fNIR and electroencephalography (EEG) signals has become the best choice of accurate brain-computer interface (BCI) because of their finer spatiotemporal resolution. The purpose of this work is to develop an effective BCI model to classify the brain signals (fNIR and EEG) regarding the voluntary and imagery movements. For achieving the high classification accuracy from the developed BCI system, Convolutional neural network (CNN) has been used to extract the features automatically from the multiple channel fNIR and EEG signals instead of the manual feature selection. In this work, eight different movement-related stimuli (four voluntary and four imagery movements of hands and feet) have been considered. The multiple channel fNIR and EEG signals are used to prepare functional neuroimages to train and test the performance of the proposed BCI system. In addition, the proposed procedure is applied to prepare neuroimages from the individual modality (fNIR and EEG) to train and test the performance of the CNN based BCI system. The results reveal that the combined-modality approach of fNIR and EEG provides improved classification accuracy than the individual one. From the results, we found that the proposed CNN-based BCI system of bimodal (fNIR+EEG) approach outperforms the unimodal (only fNIR) methods in terms of the classification accuracy. Therefore, the outcomes of the proposed research work will be very helpful to implement the finer BCI system, in future.
Study of Stimulation Effects on Different Bands of EEG Signal
(Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh., 2016-09) Ghosh, Tarun Kanti; Ahmad, Prof. Dr. Mohiuddin
Brain–computer interfaces (BCIs) are the communication bridge between the human brain and a computer which may be implemented on the basis of steady-state evoked potentials (SSEPs). A brain–computer interface is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. The objectives of the research is to check the different stimulation effects of human brain, to improve the accuracy, higher information transfer rate (ITR), desired bandwidth (BW), and signal to noise ratio (SNR) of BCIs and to identify Power and Energy of different Stimulation Effects on Alpha and Beta bands of EEG signal. In this research the power and energy of Alpha and Beta bands of EEG signal for different stimulation were determined and signal analysis, signal processing, Fast Fourier Transform (FFT), Statistical parameter methods were used. The performance of stimulator depends on many factors such as size and shape of stimulator, frequency of stimulation, luminance, color, and subject attention. Information Transfer Rate (ITR) varies with the change of frequency and size of the visual stimuli. In this research, a circular repetitive visual stimulator (CRVS) of different diameter (2", 2.5"and 3"), color (RGB), frequency (10, 15 and 20 Hz) was used. When the size of the stimulator changes from 2" to 2.5" a greater increase of Alpha wave (58.18%) is observed than Beta wave (13.68%). But a further increase in size from 2" to 2.5" a greater decrease of Alpha wave (45%) is observed than Beta wave (36.59%).