Turgay Kaya

The FIR filter design by using window parameters calculated with GA

In this study, digital filter design was realized helping window functions that their parameter values were calculated using GA. First in the study, the Kaiser window function parameters used many application was found with GA. This values obtained were used in FIR filter design and filter coefficient values which provide desired conditions were calculated. The amplitude responses obtained from available commands and filter amplitude response according to FIR filter coefficient values founded with programme were compared and plotted. The results have shown that the improved programme has been successful. Besides, this study is to provide decreasing of process steps for calculation of window function coefficients used at different filter conditions.

Wavelet-based study of valence–arousal model of emotions on EEG signals with LabVIEW

This paper illustrates the wavelet-based feature extraction for emotion assessment using electroencephalogram (EEG) signal through graphical coding design. Two-dimensional (valence–arousal) emotion model was studied. Different emotions (happy, joy, melancholy, and disgust) were studied for assessment. These emotions were stimulated by video clips. EEG signals obtained from four subjects were decomposed into five frequency bands (gamma, beta, alpha, theta, and delta) using “db5” wavelet function. Relative features were calculated to obtain further information. Impact of the emotions according to valence value was observed to be optimal on power spectral density of gamma band. The main objective of this work is not only to investigate the influence of the emotions on different frequency bands but also to overcome the difficulties in the text-based program. This work offers an alternative approach for emotion evaluation through EEG processing. There are a number of methods for emotion recognition such as wavelet transform-based, Fourier transform-based, and Hilbert–Huang transform-based methods. However, the majority of these methods have been applied with the text-based programming languages. In this study, we proposed and implemented an experimental feature extraction with graphics-based language, which provides great convenience in bioelectrical signal processing.

LabVIEW-based analysis of EEG signals in determination of sleep stages

The aim of this study is to create an interface for classification of sleep stages. Classification was made by examining of Electroencephalogram (EEG) that plays an important role in understanding and treatment of various diseases. The user interface was generated by means of Laboratory Virtual Instrument Engineering Workbench (LabVIEW) that has visual programming language. EEG signals in the time, frequency and time-frequency domain were analyzed by generated interface. Also EEG signals were studied by using MATLAB as well as LabVIEW. The control of interface was made by using signals that received from Firat University Hospital, signals which found in software and the generated signals by LabVIEW EEG simulator.

Control of robotic hand simulator via human hand motion analysis based on EMG

Powered hand prostheses with many degrees of freedom have been researching advanced and intelligent powered hand prostheses for prosthetics. Electromyography (EMG) is widely used to design prostheses for its benefit to get valuable information about the neuromuscular activity of a muscle. This paper suggests the real time tracking algorithm for human hand motion by processing the signals of surface EMG sensors attached on brachioradialis muscle. The signals acquired by using surface EMG sensors are processed with preprocesing step and filtering noises out with a bandwith filter. The processed EMG signals are directly used for the motion generation of hand in real time simulator. The hand of simülatör which designed MATLAB GUI, has fourteen degrees of freedom and complies with the desired flexion/extension motions of both the finger of human. The EMG signal recorded via EMG Arduino Shield, is a low-cost and adaptable system. The system yields quick responses and is being a valuable tool for biomedical research and development.

Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Heart Rate Variability (HRV) signal which is providing information about variation between consecutive heartbeats has been employed for extracting the parameter related to Congestive Heart Failure (CHF) from Electrocardiography (ECG). There are several studies on HRV analysis and CHF however most of the studies were performed by text-based methods. The main objective of this work is not only search to extract parameters but also to accomplish the difficulties in the text based program. This study presents graphical programming language to investigate features of HRV. Graphical User Interface (GUI) has been developed in LabVIEW to create some method for extraction features related to CHF. HRV are analyzed in the time and frequency domains. Parameters related to the time and frequency domains are derived for healthy ECG and ECG signals having Congestive Heart Failure. Several parameters which are necessary for classification algorithms are obtained.

Analysis of normal and epileptic EEG signals with filtering methods

EEG signals taken from brain surface have low amplitude and low frequency band and are nonstationary biological signals. The other electrical signals in the environment and originated from the voluntary-involuntary movements of the person may cause additional noise. So EEG records, like other biological signals, requires accurate measurement. In this study, normal/ epileptic EEG signals in relating to the WAG / Rij rats, high frequency and low frequency noise were suppressed by moving average filter and derivative-based filter, respectively. Then, the frequency spectrum of the signals were presented. Thus, it was aimed to diagnose illness by comparing the frequency band of signals concentrated intended.

The Real Time Implementation of a Chaotic System’s Synchronization for Secure Communication

The chaotic systems are preferred in secure communication systems as well as used in many different implementation areas. Real time chaos synchronization is also required for secure communication systems. In this study, the Master–Slave synchronization of chaotic Chen system was performed on a real time implementation for the secure communication systems. Active control method was used in the design of controller required for the realization of synchronization. The successful realization of the Master-Slave synchronization of the Chen system by using the controller designed on MATLAB/Simulink was shown by the obtained simulation results. Moreover, by using analogue outputs of NI-DAQ card, the Master-Slave synchronization of the Chen system was obtained and secure communication was achieved on a real time basis under LabVIEW environment.

True Random Number Generation from Bioelectrical and Physical Signals

It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

Design of digital IIR filter using Particle Swarm Optimization

The paper aims to establish a solution methodology for the optimal design of digital Infinite Impulse Response (IIR) filter by integrating the features of Particle Swarm Optimization (PSO). PSO is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality to mathematical formula over the particles position and velocity. The applied method explores the search space locally as well as globally and uses them in the mathematical formulas. The optimal design of IIR filter is realized with the result obtained with PSO.

Wavelet-based analysis method for heart rate detection of ECG signal using LabVIEW

LabVIEW is a graphical programming language that uses a dataflow model instead of sequential lines of text code. LabVIEW allows multiple operations to work in parallel. So, designers spend less time than a text based programming language. Application areas such as signal processing, image processing and data analysis are available. In this paper, wavelet analysis is used for the elimination of undesired frequency noise. In the obtained noise-free signal, the accurate heart rate was determined with helping of the program developed in the LabVIEW environment. The performance of the system was tested with different wavelet types and satisfactory results were obtained.