# Processing and spectral analysis of the raw EEG signal from the MindWave

 To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video
Save this PDF as:

Size: px
Start display at page:

Download "Processing and spectral analysis of the raw EEG signal from the MindWave"

## Transcription

3 by Discrete Fourier ransform (DF). DF transforms the sequence of N complex numbers x, x1,..., x N 1 (the time domain) into an N-periodic sequence X, X 1, X,..., X N 1 (the list of coefficient of a finite combination of complex sinusoids, ordered by their frequencies). It is according to the DF formula [1] (1): N 1 jkn / N (1) X k xne. n Each X k element encodes amplitude and phase of a sinusoidal component of function x n. Inverse conversion to the DF is the Inverse DF (IDF). he IDF transforms data from the frequency domain to the time domain. It is according to the IDF formula (11): (11) 1 1 N jkn / N xn X k e. N k In this paper, A Fast Fourier ransform (FF) is used as efficient algorithm to compute the DF and its inverse (IFF to compute IDF) [1, ]. Computational complexity is O ( N ) for standard DF and N(log N) for FF procedure. FF algorithm is based on Divide and Conquer algorithm. It divides the transform of size N to transform the size N 1 and N. In this paper, the FF algorithm is using for size of sample, according formula (1): k (1) N, where k is a natural number. In MALAB environment, the FF and IFF algorithms are implemented. Function fft(data, N) has two important input. Data is a vector of signal and N is a size of transform. If N is less than length of Data, the sequence x is truncated. If N is greater than length of Data, vector x is padded with trailing zeros to length N. his function returns the DF of vector x, computed with the FF algorithm. Function ifft(data, N) works in the same way, but returns values from a time domain. Experiments and Results In this section, two samples of the raw EEG signal are investigated. On the basis of the proposed communications protocol, the samples by 4-second are recorded. he sampling frequency is defined as 51 Hz. Next, the signals are divided into 1 equal parts (each part - 4-second). Afterwards, for each signal one of the parts is selected as the representative of the signal, which has most similar spectrum to spectrum of the entire signal. Hereinafter, these two parts will be called the sample #1 and the sample #. he sample #1 is recorded during a reading of specialized text. It is a example of moderate intellectual effort case with open eyes (it can cause increasing the amount of ocular artifacts). In figure 4 the sample #1 in the time domain is presented. he values of voltage are in the range from -18 to 63 V and the average value of the signal voltage is V. he sample # is recorded during a being in a state of relaxation. It was realized using a darkened room, closed eyes and relaxing music (no possible ocular artifacts in the signal). In figure 5 the sample # in the time domain is presented. he values of voltage are in the range from -31 to 48 V and the average value of the signal voltage is V. General characteristics are near for both sample. he maximum amplitude of the signal is similar for both samples, but they are displaced relative to each other time Fig. 4 he sample #1 of the raw EEG signal in the time domain time Fig. 5 he sample # of the raw EEG signal in the time domain. he both signals are transformed from the time domain to frequency domain for spectral analysis [11]. he function fft() is obtained with the all values of sample and parameter (1) equal 48. In the figure 6 and 7 the spectrums of frequency components are presented. power frequency Fig. 6 he spectrum of frequency components for the sample #1. power frequency Fig. 7 he spectrum of frequency components for the sample #1. he highest components of the signals have more power in the sample #1 than sample #. his is caused by the presence of the gamma wave. It is defined as rhythms from 5 to 1 Hz, but commonly is used in the range from 4 to 8 Hz. he summary of the fundamental parameters of the basic brain waves is presented [1, 13] in table 1. ab 1. he summary of the basic human brain waves. Name of the wave Frequency [Hz] ypical amplitude [ V ] Delta heta 4 8 higher than 3 Alpha or higher Beta 13 3 or higher Gamma or higher he gamma waves can be implicated in creating the unity of conscious perception. It is supposed that this wave is responsible for the integration of different sensory modalities. o show the progress of this wave ifft() function is used, but before the all values of the components from outside of the range (4 8 Hz) are reset. In that way, the comparison of the gamma wave for the both samples are presented in figure 8 and 9. A higher activity of gamma wave in the sample #1 is observed. his is caused by a less strain on the brain in the sample #. If human is in a relaxed but aware state, the alpha waves are active [7]. Figure 1 and 11 are presented the alpha waves activity for the investigated samples. he higher activity of the alpha wave in sample # is observed. his is a consequence of the less mental activity for sample #. PRZEGLĄD ELEKROECHNICZNY, ISSN 33-97, R. 9 NR /14 171

4 Fig. 8 he gamma wave for the sample #1 in the time domain Fig. 9 he gamma wave for the sample # in the time domain Fig. 1 he alpha wave for the sample #1 in the time domain Fig. 11 he alpha wave for the sample # in the time domain. he strongest brain activity is the delta wave. It is characterized by very low frequency up to 4 Hz and the amplitude higher than 1 V. he delta waves are usually associated with the deepest stages of sleep and aid in characterizing the depth of sleep. he both samples are rather weak in a comparison to the typical values of the amplitude. he variation of the delta waves is presented in figure 1 and Fig. 1 he delta wave for the sample #1 in the time domain Fig. 13 he delta wave for the sample # in the time domain. he theta waves are activated during a meditation, trance, hypnosis, intense dreams and intense emotions. In figure 14 and 15 the theta waves for the both samples are presented. he theta state is closer to relaxation state from sample #. he difference is not large, but it is clear Fig. 14 he theta wave for the sample #1 in the time domain Fig. 15 he theta wave for the sample # in the time domain. he last band of frequency of the spectral analysis, which will be analyzed, is called the beta waves. It responds of a human brain on the excited state. his may be due for example by a solving puzzles and other excited states. In the following experiment, it is the most similar pattern for the both samples. As it is showed in figure 16 and 17 the difference are very small, but for sample #1 the total power is a little higher, than in sample # (here we have only three strong spakes) Fig. 16 he beta wave for the sample #1 in the time domain Fig. 17 he beta wave for the sample # in the time domain. he proposed approach was presented and was verified as the result of the comparison between the different brain waves in two different cases (different states of mental). he all observations are consistent with the EEG theory. Naturally, the MindWave has a measurement error greater than the clinical devices, but it allows to made a basic study of the brain activity. Conclusion his paper present how to record and process the raw EEG signal from the MindWave MW1 in the MALAB environment. For this purpose, the Fourier analysis has been used as an efficient way to make the spectral analysis. he first three section describe the theoretical assumptions. It is all used to perform the simple experiment. wo different mental state have been used to record and process the two samples of the raw EEG signal. On the basis of the spectral analysis the five kind of waves were identified. Afterwards, the samples were compared in respect of these waves. he observed differences are confirmed with theory knowledge. In the result, the paper is presented the approach to efficient record and process the EEG signal with low financial outlay. his approach can be used for the neurofeedback and the brain control interface (BCI). he error of the device is too high for clinical use. REFERENCES [1] Aydemir O., Pourzare S., Kayikcioglu., Classifying Various EMG and EOG Artifacts in EEG Signals, Przegląd Elektrotechniczny, 88(11a), pp. 18-, 1. [] Cooley J. W., J. W. ukey, An Algorithm for the Machine Computation of the Complex Fourier Series, Mathematics of Computation, 19, pp , [3] Crowley K., Sliney A., Pitt I., Murphy D., Evaluating a Brain- Computer Interface to Categorize Human Emotional Response, 17 PRZEGLĄD ELEKROECHNICZNY, ISSN 33-97, R. 9 NR /14

5 1th IEEE International Conference on Advanced Learning echnologies, pp , 1. [4] Filipowicz S., Nita K., Układ elektroencefalografu przenośnego do lokalizacji aktywności elektrycznej, Przegląd Elektrotechniczny, 83(1), pp , 7 [5] Haas, L. F., Hans Berger ( ), Richard Caton ( ), and electroencephalography, Journal of Neurology, Neurosurgery & Psychiatry, 74(1), pp. 9, 3. [6] Hardy G. H., Rogosinski W. W., Fourier Series, Dover Books on Mathematics, Dover Publications, 13. [7] Hussin S. S., Sudirman R., Sensory Response through EEG Interpretation on Alpha Wave and Power Spectrum, Procedia Engineering 53, pp , 13. [8] Kołodziej M., Majkowski A., Rak R., Linear discriminant analysis as a feature reduction technique of EEG signal for braincomputer interfaces, Przegląd Elektrotechniczny, 88(3a), pp , 1. [9] Kranczioch C., Zich C., Schierholz I., Sterr A., Mobile EEG and its potential to promote the theory and application of imagerybased motor rehabilitation, International Journal of Psychophysiology, pp. 1-6, available online 18 Oct 13. [1] Oppenheim A. V., Schafer R. W., Discrete-ime Signal Processing, Prentice-Hall, p , [11] Rak R., Kołodziej M., Zastosowanie analizy częstotliwościowej sygnału EEG w interfejsach mózg-komputer, 84(5), pp , 8. [1] Rejer I., Pelczar M., Analiza statystyczna zbioru sygnałów EEG na potrzeby budowy interfejsu mózg-komputer, Wybrane zagadnienia informatyki medycznej, Stowarzyszenie Przyjaciół Wydziału Informatyki w Szczecinie, pp , 1. [13] Rejer I., EEG feature selection for BCI based on motor imaginary task, Foundations of Computing and Decision Sciences, 37(4), pp , 1. [14] Sneddon I. N., Fourier ransform, Dover Books on Mathematics, Dover Publications, 1. [15] Valer J., Daisuke., Ippeita D., 1/, 1/1, and 1/5 systems revisited: heir validity as relative head-surface-based positioning systems, NeuroImage, 34(4), pp , 7. [16] Vourvopoulos A., Liarokapis F, Evaluation of commercial brain computer interfaces in real and virtual world environment: A pilot study, Computers & Electrical Engineering, available online 1 November 13. [17] Wali M. K., Murugappan M., Badlishah Ahmad R., Classification of Driver Drowsiness Level using Wireless EEG, Przegląd Elektrotechniczny, 89(6), pp , 13. Author: Mgr inż. Wojciech Sałabun, Department of Artificial Intelligence Methods and Applied Mathematics, Faculty of Computer Science and Information echnology, West Pomeranian University of echnology, Szczecin, ul. Żołnierska 49, 71-1 Szczecin, Poland, PRZEGLĄD ELEKROECHNICZNY, ISSN 33-97, R. 9 NR /14 173

### Brain Controlled Prosthetic Leg

ISSN: 2454-132X Impact factor: 4.295 (Volume2, Issue6) Available online at: www.ijariit.com Brain Controlled Prosthetic Leg Manu R 1, Mithu Markose 2, Tonio P Thomas 3 1 B.Tech student Dept. of Electronics

### Fourier Analysis. Nikki Truss. Abstract:

Fourier Analysis Nikki Truss 09369481 Abstract: The aim of this experiment was to investigate the Fourier transforms of periodic waveforms, and using harmonic analysis of Fourier transforms to gain information

### TECHNICAL SPECIFICATIONS, VALIDATION, AND RESEARCH USE CONTENTS:

TECHNICAL SPECIFICATIONS, VALIDATION, AND RESEARCH USE CONTENTS: Introduction to Muse... 2 Technical Specifications... 3 Research Validation... 4 Visualizing and Recording EEG... 6 INTRODUCTION TO MUSE

### Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms. NeuroSky Inc.

, Vol. 12, No 3, pp.341-350, September 2009 Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms KooHyoung Lee NeuroSky Inc. khlee@neurosky.com

### Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals. Introduction

Computer Networks and Internets, 5e Chapter 6 Information Sources and Signals Modified from the lecture slides of Lami Kaya (LKaya@ieee.org) for use CECS 474, Fall 2008. 2009 Pearson Education Inc., Upper

### ANIMA: Non-Conventional Interfaces in Robot Control Through Electroencephalography and Electrooculography: Motor Module

Ninth LACCEI Latin American and Caribbean Conference (LACCEI 2011), Engineering for a Smart Planet, Innovation, Information Technology and Computational Tools for Sustainable Development, August 3-5, 2011,

### Brain Computer Interfaces (BCI) Communication Training of brain activity

Brain Computer Interfaces (BCI) Communication Training of brain activity Brain Computer Interfaces (BCI) picture rights: Gerwin Schalk, Wadsworth Center, NY Components of a Brain Computer Interface Applications

### Implementation of FIR Filter using Adjustable Window Function and Its Application in Speech Signal Processing

International Journal of Advances in Electrical and Electronics Engineering 158 Available online at www.ijaeee.com & www.sestindia.org/volume-ijaeee/ ISSN: 2319-1112 Implementation of FIR Filter using

### Curve Fitting. Next: Numerical Differentiation and Integration Up: Numerical Analysis for Chemical Previous: Optimization.

Next: Numerical Differentiation and Integration Up: Numerical Analysis for Chemical Previous: Optimization Subsections Least-Squares Regression Linear Regression General Linear Least-Squares Nonlinear

### Chapter 3 Discrete-Time Fourier Series. by the French mathematician Jean Baptiste Joseph Fourier in the early 1800 s. The

Chapter 3 Discrete-Time Fourier Series 3.1 Introduction The Fourier series and Fourier transforms are mathematical correlations between the time and frequency domains. They are the result of the heat-transfer

### The continuous and discrete Fourier transforms

FYSA21 Mathematical Tools in Science The continuous and discrete Fourier transforms Lennart Lindegren Lund Observatory (Department of Astronomy, Lund University) 1 The continuous Fourier transform 1.1

### FEATURE EXTRACTION OF EEG SIGNAL USING WAVELET TRANSFORM FOR AUTISM CLASSIFICATION

FEATURE EXTRACTION OF EEG SIGNAL USING WAVELET TRANSFORM FOR AUTISM CLASSIFICATION Lung Chuin Cheong, Rubita Sudirman and Siti Suraya Hussin Faculty of Electrical Engineering, Universiti Teknologi Malaysia

### Fast Fourier Transforms and Power Spectra in LabVIEW

Application Note 4 Introduction Fast Fourier Transforms and Power Spectra in LabVIEW K. Fahy, E. Pérez Ph.D. The Fourier transform is one of the most powerful signal analysis tools, applicable to a wide

### REALIZATION OF FAST ACQUISITION FOR SPREAD SPECTRUM SIGNAL BASED ON FFT

REALIZATION OF FAST ACQUISITION FOR SPREAD SPECTRUM SIGNAL BASED ON FFT QI Jian-zhong, Gong Yang, Song Peng College of Information Engineering, North China University of Technology, China ABSTRACT Acquisition

### A Tutorial on Fourier Analysis

A Tutorial on Fourier Analysis Douglas Eck University of Montreal NYU March 26 1.5 A fundamental and three odd harmonics (3,5,7) fund (freq 1) 3rd harm 5th harm 7th harmm.5 1 2 4 6 8 1 12 14 16 18 2 1.5

### IMPLEMENTATION OF FIR FILTER USING EFFICIENT WINDOW FUNCTION AND ITS APPLICATION IN FILTERING A SPEECH SIGNAL

IMPLEMENTATION OF FIR FILTER USING EFFICIENT WINDOW FUNCTION AND ITS APPLICATION IN FILTERING A SPEECH SIGNAL Saurabh Singh Rajput, Dr.S.S. Bhadauria Department of Electronics, Madhav Institute of Technology

### Signal Processing for Speech Recognition

Signal Processing for Speech Recognition Once a signal has been sampled, we have huge amounts of data, often 20,000 16 bit numbers a second! We need to find ways to concisely capture the properties of

### EECC694 - Shaaban. Transmission Channel

The Physical Layer: Data Transmission Basics Encode data as energy at the data (information) source and transmit the encoded energy using transmitter hardware: Possible Energy Forms: Electrical, light,

### International Journal of Advanced Research in Computer Science and Software Engineering

Volume 2, Issue 8, August 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Development of

### Department of Electrical and Computer Engineering Ben-Gurion University of the Negev. LAB 1 - Introduction to USRP

Department of Electrical and Computer Engineering Ben-Gurion University of the Negev LAB 1 - Introduction to USRP - 1-1 Introduction In this lab you will use software reconfigurable RF hardware from National

### 5 Signal Design for Bandlimited Channels

225 5 Signal Design for Bandlimited Channels So far, we have not imposed any bandwidth constraints on the transmitted passband signal, or equivalently, on the transmitted baseband signal s b (t) I[k]g

### ANALYSIS AND APPLICATIONS OF LAPLACE /FOURIER TRANSFORMATIONS IN ELECTRIC CIRCUIT

www.arpapress.com/volumes/vol12issue2/ijrras_12_2_22.pdf ANALYSIS AND APPLICATIONS OF LAPLACE /FOURIER TRANSFORMATIONS IN ELECTRIC CIRCUIT M. C. Anumaka Department of Electrical Electronics Engineering,

### Discrete Fourier Series & Discrete Fourier Transform Chapter Intended Learning Outcomes

Discrete Fourier Series & Discrete Fourier Transform Chapter Intended Learning Outcomes (i) Understanding the relationships between the transform, discrete-time Fourier transform (DTFT), discrete Fourier

### Conceptual similarity to linear algebra

Modern approach to packing more carrier frequencies within agivenfrequencyband orthogonal FDM Conceptual similarity to linear algebra 3-D space: Given two vectors x =(x 1,x 2,x 3 )andy = (y 1,y 2,y 3 ),

### Broadband Networks. Prof. Dr. Abhay Karandikar. Electrical Engineering Department. Indian Institute of Technology, Bombay. Lecture - 29.

Broadband Networks Prof. Dr. Abhay Karandikar Electrical Engineering Department Indian Institute of Technology, Bombay Lecture - 29 Voice over IP So, today we will discuss about voice over IP and internet

### Classic EEG (ERPs)/ Advanced EEG. Quentin Noirhomme

Classic EEG (ERPs)/ Advanced EEG Quentin Noirhomme Outline Origins of MEEG Event related potentials Time frequency decomposition i Source reconstruction Before to start EEGlab Fieldtrip (included in spm)

### ANALYZER BASICS WHAT IS AN FFT SPECTRUM ANALYZER? 2-1

WHAT IS AN FFT SPECTRUM ANALYZER? ANALYZER BASICS The SR760 FFT Spectrum Analyzer takes a time varying input signal, like you would see on an oscilloscope trace, and computes its frequency spectrum. Fourier's

### Fourier Analysis. A cosine wave is just a sine wave shifted in phase by 90 o (φ=90 o ). degrees

Fourier Analysis Fourier analysis follows from Fourier s theorem, which states that every function can be completely expressed as a sum of sines and cosines of various amplitudes and frequencies. This

### SIGNAL PROCESSING & SIMULATION NEWSLETTER

1 of 10 1/25/2008 3:38 AM SIGNAL PROCESSING & SIMULATION NEWSLETTER Note: This is not a particularly interesting topic for anyone other than those who ar e involved in simulation. So if you have difficulty

### Laboratory Assignment 4. Fourier Sound Synthesis

Laboratory Assignment 4 Fourier Sound Synthesis PURPOSE This lab investigates how to use a computer to evaluate the Fourier series for periodic signals and to synthesize audio signals from Fourier series

### Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep

Engineering, 23, 5, 88-92 doi:.4236/eng.23.55b8 Published Online May 23 (http://www.scirp.org/journal/eng) Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep JeeEun

### Topic 4: Continuous-Time Fourier Transform (CTFT)

ELEC264: Signals And Systems Topic 4: Continuous-Time Fourier Transform (CTFT) Aishy Amer Concordia University Electrical and Computer Engineering o Introduction to Fourier Transform o Fourier transform

### TCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS

TCOM 370 NOTES 99-4 BANDWIDTH, FREQUENCY RESPONSE, AND CAPACITY OF COMMUNICATION LINKS 1. Bandwidth: The bandwidth of a communication link, or in general any system, was loosely defined as the width of

### The Fast Fourier Transform (FFT) and MATLAB Examples

The Fast Fourier Transform (FFT) and MATLAB Examples Learning Objectives Discrete Fourier transforms (DFTs) and their relationship to the Fourier transforms Implementation issues with the DFT via the FFT

### A Web-Based System for EEG Data Visualization and Analysis

Int'l Conf. Health Informatics and Medical Systems HIMS'15 119 A Web-Based System for EEG Data Visualization and Analysis A. Jonathan Garza, B. Sishir Subedi, C. Yuntian Zhang and D. Hong Lin Department

### Spectrum Level and Band Level

Spectrum Level and Band Level ntensity, ntensity Level, and ntensity Spectrum Level As a review, earlier we talked about the intensity of a sound wave. We related the intensity of a sound wave to the acoustic

### Digital image processing

Digital image processing The two-dimensional discrete Fourier transform and applications: image filtering in the frequency domain Introduction Frequency domain filtering modifies the brightness values

### Introduction to acoustic imaging

Introduction to acoustic imaging Contents 1 Propagation of acoustic waves 3 1.1 Wave types.......................................... 3 1.2 Mathematical formulation.................................. 4 1.3

### SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY

3 th World Conference on Earthquake Engineering Vancouver, B.C., Canada August -6, 24 Paper No. 296 SOFTWARE FOR GENERATION OF SPECTRUM COMPATIBLE TIME HISTORY ASHOK KUMAR SUMMARY One of the important

### FOURIER TRANSFORM BASED SIMPLE CHORD ANALYSIS. UIUC Physics 193 POM

FOURIER TRANSFORM BASED SIMPLE CHORD ANALYSIS Fanbo Xiang UIUC Physics 193 POM Professor Steven M. Errede Fall 2014 1 Introduction Chords, an essential part of music, have long been analyzed. Different

### Frequency Response of FIR Filters

Frequency Response of FIR Filters Chapter 6 This chapter continues the study of FIR filters from Chapter 5, but the emphasis is frequency response, which relates to how the filter responds to an input

### A DESIGN OF DSPIC BASED SIGNAL MONITORING AND PROCESSING SYSTEM

ISTANBUL UNIVERSITY JOURNAL OF ELECTRICAL & ELECTRONICS ENGINEERING YEAR VOLUME NUMBER : 2009 : 9 : 1 (921-927) A DESIGN OF DSPIC BASED SIGNAL MONITORING AND PROCESSING SYSTEM Salih ARSLAN 1 Koray KÖSE

### The Calculation of G rms

The Calculation of G rms QualMark Corp. Neill Doertenbach The metric of G rms is typically used to specify and compare the energy in repetitive shock vibration systems. However, the method of arriving

### MindWave Mobile: User Guide

MindWave Mobile: User Guide e NeuroSky product families consist of hardware and software components for simple integration of this biosensor technology into consumer and industrial end-applications. All

### Data Transmission. Data Communications Model. CSE 3461 / 5461: Computer Networking & Internet Technologies. Presentation B

CSE 3461 / 5461: Computer Networking & Internet Technologies Data Transmission Presentation B Kannan Srinivasan 08/30/2012 Data Communications Model Figure 1.2 Studying Assignment: 3.1-3.4, 4.1 Presentation

### Kenrico Sap Sheet - Brain Wave Research. Clinical Study (June 6th, 2003) Test article: Kenrico Sap Sheet TRMX (Tourmaline) Test device: EEG

Kenrico Sap Sheet - Brain Wave Research Clinical Study (June 6th, 2003) Test article: Kenrico Sap Sheet TRMX (Tourmaline) Test device: EEG An electroencephalography (EEG) (elek- tro- in- SEF- all- oh-

### Acoustic Processor of the MCM Sonar

AUTOMATYKA/ AUTOMATICS 2013 Vol. 17 No. 1 http://dx.doi.org/10.7494/automat.2013.17.1.73 Mariusz Rudnicki*, Jan Schmidt*, Aleksander Schmidt*, Wojciech Leœniak* Acoustic Processor of the MCM Sonar 1. Introduction

### USE OF SYNCHRONOUS NOISE AS TEST SIGNAL FOR EVALUATION OF STRONG MOTION DATA PROCESSING SCHEMES

USE OF SYNCHRONOUS NOISE AS TEST SIGNAL FOR EVALUATION OF STRONG MOTION DATA PROCESSING SCHEMES Ashok KUMAR, Susanta BASU And Brijesh CHANDRA 3 SUMMARY A synchronous noise is a broad band time series having

### Sampling Theorem Notes. Recall: That a time sampled signal is like taking a snap shot or picture of signal periodically.

Sampling Theorem We will show that a band limited signal can be reconstructed exactly from its discrete time samples. Recall: That a time sampled signal is like taking a snap shot or picture of signal

### Understand the principles of operation and characterization of digital filters

Digital Filters 1.0 Aim Understand the principles of operation and characterization of digital filters 2.0 Learning Outcomes You will be able to: Implement a digital filter in MATLAB. Investigate how commercial

### Fourier Analysis. Evan Sheridan, Chris Kervick, Tom Power Novemeber

Fourier Analysis Evan Sheridan, Chris Kervick, Tom Power 11367741 Novemeber 19 01 Abstract Various properties of the Fourier Transform are investigated using the Cassy Lab software, a microphone, electrical

### T = 1 f. Phase. Measure of relative position in time within a single period of a signal For a periodic signal f(t), phase is fractional part t p

Data Transmission Concepts and terminology Transmission terminology Transmission from transmitter to receiver goes over some transmission medium using electromagnetic waves Guided media. Waves are guided

### Interferometric Dispersion Measurements

Application Note 2004-022A Interferometric Dispersion Measurements Overview This application note describes how the dbm 2355 Dispersion Measurement Module operates. Summary There are two primary methods

### The Complex Fourier Transform

CHAPTER 31 The Complex Fourier Transform Although complex numbers are fundamentally disconnected from our reality, they can be used to solve science and engineering problems in two ways. First, the parameters

Modbus Frequently Asked Questions WP-34-REV0-0609-1/7 The Answer to the 14 Most Frequently Asked Modbus Questions Exactly what is Modbus? Modbus is an open serial communications protocol widely used in

### FIR Filter Design. FIR Filters and the z-domain. The z-domain model of a general FIR filter is shown in Figure 1. Figure 1

FIR Filters and the -Domain FIR Filter Design The -domain model of a general FIR filter is shown in Figure. Figure Each - box indicates a further delay of one sampling period. For example, the input to

### MATLAB for Audio Signal Processing. MATLAB for Audio Signal Processing. Aliasing. Getting data into the computer

MATLAB for Audio Signal Processing P. Professorson UT Arlington Night School MATLAB for Audio Signal Processing Getting real world data into your computer Analysis based on frequency content Fourier analysis

### RightMark Audio Analyzer

RightMark Audio Analyzer Version 2.5 2001 http://audio.rightmark.org Tests description 1 Contents FREQUENCY RESPONSE TEST... 2 NOISE LEVEL TEST... 3 DYNAMIC RANGE TEST... 5 TOTAL HARMONIC DISTORTION TEST...

### Synchronization of sampling in distributed signal processing systems

Synchronization of sampling in distributed signal processing systems Károly Molnár, László Sujbert, Gábor Péceli Department of Measurement and Information Systems, Budapest University of Technology and

### Speech Signal Processing: An Overview

Speech Signal Processing: An Overview S. R. M. Prasanna Department of Electronics and Electrical Engineering Indian Institute of Technology Guwahati December, 2012 Prasanna (EMST Lab, EEE, IITG) Speech

### Rapid Eye Movement Detection using Support Vector Machine. Cristian H. Díaz, Leonardo A. Causa, Javier J. Causa and Claudio M.

Rapid Eye Movement Detection using Support Vector Machine Cristian H. Díaz, Leonardo A. Causa, Javier J. Causa and Claudio M. Held INTRODUCTION Sleep states and stages are identified by the presence of

### This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.

This document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore. Title Transcription of polyphonic signals using fast filter bank( Accepted version ) Author(s) Foo, Say Wei;

### Non-Data Aided Carrier Offset Compensation for SDR Implementation

Non-Data Aided Carrier Offset Compensation for SDR Implementation Anders Riis Jensen 1, Niels Terp Kjeldgaard Jørgensen 1 Kim Laugesen 1, Yannick Le Moullec 1,2 1 Department of Electronic Systems, 2 Center

### Using EEG to Improve Massive Open Online Courses Feedback Interaction

Using EEG to Improve Massive Open Online Courses Feedback Interaction Haohan Wang, Yiwei Li, Xiaobo Hu, Yucong Yang, Zhu Meng, Kai-min Chang Language Technologies Institute School of Computer Science Carnegie

### VCO Phase noise. Characterizing Phase Noise

VCO Phase noise Characterizing Phase Noise The term phase noise is widely used for describing short term random frequency fluctuations of a signal. Frequency stability is a measure of the degree to which

### Lecture 3: Signaling and Clock Recovery. CSE 123: Computer Networks Stefan Savage

Lecture 3: Signaling and Clock Recovery CSE 123: Computer Networks Stefan Savage Last time Protocols and layering Application Presentation Session Transport Network Datalink Physical Application Transport

### Computational model of Temporal Lobe Epilepsy

Computational model of Temporal Lobe Epilepsy Piotr Mirowski From Wendling et al, 2000, 2002, 2005 CBLL meeting, February 6, 2008 [Wendling et al, 2002] 1 Brain anatomy and temporal lobe Frontal lobe Hippocampus

### Real-Time Filtering in BioExplorer

Real-Time Filtering in BioExplorer Filtering theory Every signal can be thought of as built up from a large number of sine and cosine waves with different frequencies. A digital filter is a digital signal

### Bharathwaj Muthuswamy EE100 Active Filters

Bharathwaj Muthuswamy EE100 mbharat@cory.eecs.berkeley.edu 1. Introduction Active Filters In this chapter, we will deal with active filter circuits. Why even bother with active filters? Answer: Audio.

### Voice Signal s Noise Reduction Using Adaptive/Reconfigurable Filters for the Command of an Industrial Robot

Voice Signal s Noise Reduction Using Adaptive/Reconfigurable Filters for the Command of an Industrial Robot Moisa Claudia**, Silaghi Helga Maria**, Rohde L. Ulrich * ***, Silaghi Paul****, Silaghi Andrei****

### Frequency Domain Characterization of Signals. Yao Wang Polytechnic University, Brooklyn, NY11201 http: //eeweb.poly.edu/~yao

Frequency Domain Characterization of Signals Yao Wang Polytechnic University, Brooklyn, NY1121 http: //eeweb.poly.edu/~yao Signal Representation What is a signal Time-domain description Waveform representation

### MOUSE CURSOR CONTROL SYSTEM USING ELECTROOCULOGRAM SIGNALS

World Automation Congress 2010 TSI Press. MOUSE CURSOR CONTROL SYSTEM USING ELECTROOCULOGRAM SIGNALS HIROKI TAMURA *, MASAKI MIYASHITA, KOICHI TANNO AND YASUSHI FUSE Faculty of Engineering, University

### Final Year Project Progress Report. Frequency-Domain Adaptive Filtering. Myles Friel. Supervisor: Dr.Edward Jones

Final Year Project Progress Report Frequency-Domain Adaptive Filtering Myles Friel 01510401 Supervisor: Dr.Edward Jones Abstract The Final Year Project is an important part of the final year of the Electronic

### Computing Fourier Series and Power Spectrum with MATLAB

Computing Fourier Series and Power Spectrum with MATLAB By Brian D. Storey. Introduction Fourier series provides an alternate way of representing data: instead of representing the signal amplitude as a

### Estimating Dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum

Estimating Dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum 1 Anton and Pedro Abstract Here the steps done for identification of dynamics for (DC-motor)+(1st Link) of the Furuta Pendulum are described.

### Matlab GUI for WFB spectral analysis

Matlab GUI for WFB spectral analysis Jan Nováček Department of Radio Engineering K13137, CTU FEE Prague Abstract In the case of the sound signals analysis we usually use logarithmic scale on the frequency

### 4.3 Analog-to-Digital Conversion

4.3 Analog-to-Digital Conversion overview including timing considerations block diagram of a device using a DAC and comparator example of a digitized spectrum number of data points required to describe

### An introduction to synchronous detection. A.Platil

An introduction to synchronous detection A.Platil Overview: Properties of SD Fourier transform and spectra General synchronous detector (AKA lock-in amplifier, syn. demodulator, Phase Sensitive Detector)

### Analog and Digital Signals, Time and Frequency Representation of Signals

1 Analog and Digital Signals, Time and Frequency Representation of Signals Required reading: Garcia 3.1, 3.2 CSE 3213, Fall 2010 Instructor: N. Vlajic 2 Data vs. Signal Analog vs. Digital Analog Signals

### OPEN LOOP CONTROL OF FLEXIBLE BEAM PERIODIC MOTION VIA FREQUENCY RESPONSE ANALYSIS

ABCM Symposium Series in Mechatronics - Vol. 4 - pp.7-78 Copyright Proceedings 2 of COBEM by ABCM 29 Copyright c 29 by ABCM 2th International Congress of Mechanical Engineering November 5-2, 29, Gramado,

### Sampling and Interpolation. Yao Wang Polytechnic University, Brooklyn, NY11201

Sampling and Interpolation Yao Wang Polytechnic University, Brooklyn, NY1121 http://eeweb.poly.edu/~yao Outline Basics of sampling and quantization A/D and D/A converters Sampling Nyquist sampling theorem

### Signal Processing First Lab 08: Frequency Response: Bandpass and Nulling Filters

Signal Processing First Lab 08: Frequency Response: Bandpass and Nulling Filters Pre-Lab and Warm-Up: You should read at least the Pre-Lab and Warm-up sections of this lab assignment and go over all exercises

### Email: tjohn@mail.nplindia.ernet.in

USE OF VIRTUAL INSTRUMENTS IN RADIO AND ATMOSPHERIC EXPERIMENTS P.N. VIJAYAKUMAR, THOMAS JOHN AND S.C. GARG RADIO AND ATMOSPHERIC SCIENCE DIVISION, NATIONAL PHYSICAL LABORATORY, NEW DELHI 110012, INDIA

### Chapter 8 - Power Density Spectrum

EE385 Class Notes 8/8/03 John Stensby Chapter 8 - Power Density Spectrum Let X(t) be a WSS random process. X(t) has an average power, given in watts, of E[X(t) ], a constant. his total average power is

### DSP based implementation of a Configurable Composite Digital Transmitter Soumik Kundu, Amiya Karmakar

International Journal of Scientific & Engineering Research, Volume 4, Issue 4, April-2013 262 DSP based implementation of a Configurable Composite Digital Transmitter Soumik Kundu, Amiya Karmakar Abstract

### Fig. 2 shows a simple step waveform in which switching time of power devices are not considered and assuming the switch is ideal.

CHAPTER 3: ANAYSIS OF THREE-EE INERTER In this chapter an analysis of three-level converter is presented by considering the output voltage waveform in order to determine the switching angle of power devices.

### Implementation of a 3-Dimensional Game for developing balanced Brainwave

Fifth International Conference on Software Engineering Research, Management and Applications Implementation of a 3-Dimensional Game for developing balanced Brainwave Beom-Soo Shim, Sung-Wook Lee and Jeong-Hoon

Change Your Mind with Neurofeedback By Gerry Radano, LMSW, AIBT Neurofeedback For thousands of years, people have used meditation and prayer to change the brain by training the mind. In the past 50 years,

### DIODE CIRCUITS LABORATORY. Fig. 8.1a Fig 8.1b

DIODE CIRCUITS LABORATORY A solid state diode consists of a junction of either dissimilar semiconductors (pn junction diode) or a metal and a semiconductor (Schottky barrier diode). Regardless of the type,

### Analog vs. Digital Transmission

Analog vs. Digital Transmission Compare at two levels: 1. Data continuous (audio) vs. discrete (text) 2. Signaling continuously varying electromagnetic wave vs. sequence of voltage pulses. Also Transmission

### R U S S E L L L. H E R M A N

R U S S E L L L. H E R M A N A N I N T R O D U C T I O N T O F O U R I E R A N D C O M P L E X A N A LY S I S W I T H A P P L I C AT I O N S T O T H E S P E C T R A L A N A LY S I S O F S I G N A L S R.

Measurement Lab Fourier Analysis Last Modified 9/5/06 Any time-varying signal can be constructed by adding together sine waves of appropriate frequency, amplitude, and phase. Fourier analysis is a technique

### International Journal of Computer Trends and Technology (IJCTT) volume 4 Issue 8 August 2013

A Short-Term Traffic Prediction On A Distributed Network Using Multiple Regression Equation Ms.Sharmi.S 1 Research Scholar, MS University,Thirunelvelli Dr.M.Punithavalli Director, SREC,Coimbatore. Abstract:

### Purpose of Time Series Analysis. Autocovariance Function. Autocorrelation Function. Part 3: Time Series I

Part 3: Time Series I Purpose of Time Series Analysis (Figure from Panofsky and Brier 1968) Autocorrelation Function Harmonic Analysis Spectrum Analysis Data Window Significance Tests Some major purposes

### Adaptive games for cognitive training: Lessons measuring arousal with EEG

Adaptive games for cognitive training: Lessons measuring arousal with EEG Daniel Sjölie Umeå University Umeå, SE-981 87, Sweden daniel@cs.umu.se Copyright is held by the author/owner(s). CHI 13, April

### Image Enhancement in the Frequency Domain

Image Enhancement in the Frequency Domain Jesus J. Caban Outline! Assignment #! Paper Presentation & Schedule! Frequency Domain! Mathematical Morphology %& Assignment #! Questions?! How s OpenCV?! You

### The Frequency Distribution of Quantization Error in Digitizers for Coherent Sampling Sol Neeman, Ph.D. Johnson and Wales University

The Frequency Distribution of Quantization Error in Digitizers for Coherent Sampling Sol Neeman, Ph.D. Johnson and Wales University Abstract In this paper the frequency distribution of quantization error

### Probability and Random Variables. Generation of random variables (r.v.)

Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly