This document summarizes a research paper that compares the performance of different independent component analysis (ICA) algorithms and a new technique called Cycle Spinning Wavelet-ICA Merger (CTICA) for removing artifacts from electroencephalogram (EEG) signals. It finds that CTICA performs as well as other ICA algorithms like FastICA, JADE, and Radical at denoising EEG signals. The document provides background on EEG signals, common artifacts that contaminate EEG signals, existing techniques like ICA and wavelet transforms for removing artifacts, and prior research combining ICA and wavelets. It also describes the two datasets and methodology used to test CTICA's performance.
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
This document reviews techniques for extracting features and classifying EEG signals to detect human stress levels. It discusses EEG signals and how they can provide information about mental states. It also reviews common feature extraction methods like DCT and DWT that can preprocess EEG data by transforming it from the time to frequency domain. Classification algorithms like KNN, LDA, and Naive Bayes that can classify EEG data are also examined. The document proposes a system to use a Neurosky Mindwave EEG headset to record raw EEG signals, preprocess them with DWT, and classify stress levels using a combination of classifiers.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Ffeature extraction of epilepsy eeg using discrete wavelet transformAboul Ella Hassanien
This document summarizes a presentation on feature extraction of epilepsy EEG signals using discrete wavelet transforms. The presentation discusses EEG data acquisition from public datasets containing healthy and epileptic patient recordings. It then describes using discrete wavelet transforms to decompose EEG signals into different frequency sub-bands, and extracting statistical features from each sub-band like maximum, minimum, mean, standard deviation, and entropy. These extracted features are used to classify EEG signals as normal or epileptic. The approach decomposes signals into 5 sub-bands corresponding to delta, theta, alpha, beta, and gamma frequency ranges to capture characteristics of different brain states for epilepsy identification.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Feature Extraction Techniques and Classification Algorithms for EEG Signals t...Editor IJCATR
This document reviews techniques for extracting features and classifying EEG signals to detect human stress levels. It discusses EEG signals and how they can provide information about mental states. It also reviews common feature extraction methods like DCT and DWT that can preprocess EEG data by transforming it from the time to frequency domain. Classification algorithms like KNN, LDA, and Naive Bayes that can classify EEG data are also examined. The document proposes a system to use a Neurosky Mindwave EEG headset to record raw EEG signals, preprocess them with DWT, and classify stress levels using a combination of classifiers.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
This document discusses feature extraction, classification, and prediction techniques applied to EEG data to discriminate between left and right hand movements. It first provides background on EEG signals and preprocessing. It then examines feature extraction in depth, evaluating various features like mean, standard deviation, and Hjorth parameters. Classification algorithms like LDA, KNN, and neural networks are also analyzed and compared. The best results were obtained by combining Hjorth features, achieving 74% accuracy. Future work to improve these techniques is also mentioned.
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
Variants of Support Vector
Machines (SVM) were employed for classification and also
compared the results with Multi-layered Perceptron (MLP).
Empirical results show that both SVM and MLP were suitable
for such motor imagery classifications with the accuracies 85%
and 85.71% respectively. Among all employed feature extraction
techniques wavelet-based methods specifically the energy-
entropy feature set gave promising results for both the classifiers.
Ffeature extraction of epilepsy eeg using discrete wavelet transformAboul Ella Hassanien
This document summarizes a presentation on feature extraction of epilepsy EEG signals using discrete wavelet transforms. The presentation discusses EEG data acquisition from public datasets containing healthy and epileptic patient recordings. It then describes using discrete wavelet transforms to decompose EEG signals into different frequency sub-bands, and extracting statistical features from each sub-band like maximum, minimum, mean, standard deviation, and entropy. These extracted features are used to classify EEG signals as normal or epileptic. The approach decomposes signals into 5 sub-bands corresponding to delta, theta, alpha, beta, and gamma frequency ranges to capture characteristics of different brain states for epilepsy identification.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Wavelet Based Feature Extraction Scheme Of Eeg Waveformshan pri
This document presents a project on wavelet based feature extraction of electroencephalography (EEG) signals. It discusses using wavelet transforms as an alternative to discrete Fourier transforms for feature extraction from EEG data. The objectives are to improve quality of life for those with disabilities through neuroprosthetics applications of brain-computer interfaces. Wavelet transforms provide advantages over short-time Fourier transforms like multi-resolution analysis and the ability to analyze non-stationary signals. The document outlines the methodology, which includes EEG signal acquisition, wavelet decomposition, coefficient computation, and signal reconstruction in MATLAB.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...sipij
- The document analyzes brain cognitive states during an arithmetic task and motor task using electroencephalography (EEG) signals.
- EEG data was collected from 10 healthy volunteers during resting states, a motor task, and performing arithmetic calculations.
- The EEG signals were analyzed using standardized low resolution brain electromagnetic tomography (sLORETA) to generate 3D cortical distributions and localize the neuronal generators responsible for different cognitive states.
- The results were consistent with previous neuroimaging research, showing that EEG can demonstrate neuronal activity at the cortical level with good spatial resolution and provide both spatial and temporal information about cognitive functions.
The document discusses compressive wideband power spectrum analysis for EEG signals using FastICA and neural networks. It first provides background on EEG signals and how they are measured. It then describes using FastICA to extract independent components from EEG signals related to detecting epileptic seizures. The independent components are then used to train a backpropagation neural network for effective detection of epileptic seizures. The proposed method involves preprocessing EEG signals, performing spectral estimation using FastICA, and classifying brain activity patterns using the neural network.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study that compares methods for detecting epileptic seizures from EEG signals. It presents Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) for feature extraction, and Support Vector Machines (SVM) and Neural Networks (NN) for classification. DWT decomposes signals into time-frequency components while ICA separates independent signal sources. The methods are tested on EEG data from epileptic patients, evaluating specificity, sensitivity and accuracy of seizure detection. DWT & NN achieved 99.5% accuracy between normal and seizure signals, outperforming ICA. Future work could apply these methods to other datasets and compare detection performance.
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
This document provides a review of techniques for denoising electroencephalogram (EEG) signals to remove noise and artifacts for improved analysis of brain disorders. It discusses how EEG signals are contaminated by various noise sources that can obscure important information. Several denoising techniques are examined, including independent component analysis (ICA), principal component analysis (PCA), wavelet-based denoising, and wavelet packet-based denoising. Wavelet transforms are highlighted as providing effective solutions for denoising non-stationary signals like EEG due to their ability to perform time-frequency analysis. The document concludes that wavelet methods, especially using wavelet packets, are useful for removing noise from EEG signals.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses FPGA implementation of a wavelet-based technique for denoising EEG signals. It begins by introducing EEG signals and their clinical uses. It then describes using the wavelet transform to perform data reduction and preprocessing of five classes of EEG signals (Alpha, Beta, Gamma, Delta, Theta) generated in MATLAB. Several iterations were performed to find suitable wavelet coefficients that could correctly classify all five EEG classes. Finally, the resulting parameters were implemented on an FPGA.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
Ecg beat classification and feature extraction using artificial neural networ...priyanka leenakhabiya
This document discusses a proposed technique for ECG beat classification and feature extraction using artificial neural networks and discrete wavelet transform. The key steps of the proposed technique include ECG data pre-processing using discrete wavelet transform to remove noise, extracting features such as RR interval and QRS complex, designing and training an artificial neural network on the extracted features, and using an Euclidean classifier to classify different ECG cases based on the minimum distance between features. Experimental results on ECG data from the MIT-BIH database show that the proposed technique achieves high classification accuracy and sensitivity compared to previous methods.
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...Md Kafiul Islam
This summarizes a document reviewing an integrated device for combined optical neuromodulation and electrical recording for chronic in-vivo applications. The device consists of an optical fiber integrated with a microelectrode array (MEA), called an optrode-MEA, that allows for simultaneous optical stimulation and electrophysiological recording. The summary is:
[1] The optrode-MEA was developed to allow for optical stimulation at one cortical site while recording neural activity from surrounding neurons to study local circuit dynamics. [2] Experiments in freely moving rats demonstrated the device could successfully record neural signals over months, mapping spatially and temporally resolved neuronal responses. [3] Analysis of recorded signals under different stimulation conditions proved the
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
This document discusses different types of artifacts that can appear on an EEG, including how to identify and eliminate them. It separates artifacts into physiological artifacts originating from the patient's body (e.g. eye movements, muscle activity), and extraphysiological artifacts from external sources (e.g. electrodes, equipment, environment). Specific artifact types like blinks, lateral eye movements, muscle activity are described. Guidelines provided on how to reduce artifacts include closing the eyes, relaxing muscles, ensuring good electrode contact, and shielding from environmental interference.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
Wavelet Based Feature Extraction Scheme Of Eeg Waveformshan pri
This document presents a project on wavelet based feature extraction of electroencephalography (EEG) signals. It discusses using wavelet transforms as an alternative to discrete Fourier transforms for feature extraction from EEG data. The objectives are to improve quality of life for those with disabilities through neuroprosthetics applications of brain-computer interfaces. Wavelet transforms provide advantages over short-time Fourier transforms like multi-resolution analysis and the ability to analyze non-stationary signals. The document outlines the methodology, which includes EEG signal acquisition, wavelet decomposition, coefficient computation, and signal reconstruction in MATLAB.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
My Thesis Topic was "Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface." I have done my undergraduate thesis on the study, comparison and development of newer algorithms and feature sets related to two class classification problem in Motor Imagery Signal Classification using EEG and ECoG signal for Brain Computer Interface under the supervision of Dr. Mohammad Imamul Hassan Bhuiyan, Professor, Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology.
1) The document presents research on using EEG signals to predict reaching targets during two experiments.
2) It describes the components of a BCI system and the challenges of using EEG data, which can be contaminated by artifacts from eye and muscle movements.
3) The researchers used independent component analysis and other techniques to remove artifacts from the EEG data before extracting features and using classification algorithms to decode reaching targets.
Classification of EEG Signal for Epileptic Seizure DetectionusingEMD and ELMIJTET Journal
Abstract—This paper proposes the classification of EEG signal for epilepsy diagnosis. Epilepsy is a neurological disorder which occurs due to synchronous neuronal activity in brain. Empirical Mode Decomposition (EMD), Extreme Learning Machine (ELM) are the techniquedelivered in the proposed method.Input EEG signal, which is available in online as Bonn Database is decomposed into five Intrinsic Mode Functions (IMFs) using EMD.Higher Order Statistical moments such as Variance, Skewness and Kurtosis are drawn out as features from the decomposed signals. Extreme Learning Machine is used as a classifier to classify the EEG signals with the taken features, under various categories that include healthy and ictal, interictal and ictal, Non seizure and seizure, healthy, interictal and ictal. The proposed method gives 100%accuracy, 100%sensitivity in discriminating interictal and ictal, non seizure and seizure, healthy and ictal, healthy, interictal and ictal, 100% specificity in classifying healthy and ictal, interictal and ictal and 100% and 99%accuracy in case of discriminating interictal and ictal, non seizure and seizure.
ANALYSIS OF BRAIN COGNITIVE STATE FOR ARITHMETIC TASK AND MOTOR TASK USING EL...sipij
- The document analyzes brain cognitive states during an arithmetic task and motor task using electroencephalography (EEG) signals.
- EEG data was collected from 10 healthy volunteers during resting states, a motor task, and performing arithmetic calculations.
- The EEG signals were analyzed using standardized low resolution brain electromagnetic tomography (sLORETA) to generate 3D cortical distributions and localize the neuronal generators responsible for different cognitive states.
- The results were consistent with previous neuroimaging research, showing that EEG can demonstrate neuronal activity at the cortical level with good spatial resolution and provide both spatial and temporal information about cognitive functions.
The document discusses compressive wideband power spectrum analysis for EEG signals using FastICA and neural networks. It first provides background on EEG signals and how they are measured. It then describes using FastICA to extract independent components from EEG signals related to detecting epileptic seizures. The independent components are then used to train a backpropagation neural network for effective detection of epileptic seizures. The proposed method involves preprocessing EEG signals, performing spectral estimation using FastICA, and classifying brain activity patterns using the neural network.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes a study that compares methods for detecting epileptic seizures from EEG signals. It presents Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA) for feature extraction, and Support Vector Machines (SVM) and Neural Networks (NN) for classification. DWT decomposes signals into time-frequency components while ICA separates independent signal sources. The methods are tested on EEG data from epileptic patients, evaluating specificity, sensitivity and accuracy of seizure detection. DWT & NN achieved 99.5% accuracy between normal and seizure signals, outperforming ICA. Future work could apply these methods to other datasets and compare detection performance.
Denoising of EEG Signals for Analysis of Brain Disorders: A ReviewIRJET Journal
This document provides a review of techniques for denoising electroencephalogram (EEG) signals to remove noise and artifacts for improved analysis of brain disorders. It discusses how EEG signals are contaminated by various noise sources that can obscure important information. Several denoising techniques are examined, including independent component analysis (ICA), principal component analysis (PCA), wavelet-based denoising, and wavelet packet-based denoising. Wavelet transforms are highlighted as providing effective solutions for denoising non-stationary signals like EEG due to their ability to perform time-frequency analysis. The document concludes that wavelet methods, especially using wavelet packets, are useful for removing noise from EEG signals.
The International Journal of Engineering and Science (IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Brain Computer Interface for User Recognition And Smart Home ControlIJTET Journal
This project discussed about a brain controlled biometric based on Brain–computer interfaces (BCI). BCIs are systems that can bypass conventional channels of communication (i.e., muscles and thoughts) to provide direct communication and control between the human brain and physical devices by translating different patterns of brain activity into commands in real time. With these commands a biometric technology can be controlled. The intention of the project work is to develop a user recognition machine that can assist the work independent on others. Here, we are analyzing the brain wave signals. Human brain consists of millions of interconnected neurons. The patterns of interaction between these neurons are represented as thoughts and emotional states. According to the human thoughts, this pattern will be changing which in turn produce different electrical waves. A muscle contraction will also generate a unique electrical signal. All these electrical waves will be sensed by the brain wave sensor and it will convert the data into packets and transmit through Bluetooth medium. Level analyzer unit (LAU) will receive the brain wave raw data and it will extract and process the signal using Mat lab platform. Then the control commands will be transmitted to the robotic module to process. With this entire system, we can operate the home application according to the human thoughts and it can be turned by blink muscle contraction.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses FPGA implementation of a wavelet-based technique for denoising EEG signals. It begins by introducing EEG signals and their clinical uses. It then describes using the wavelet transform to perform data reduction and preprocessing of five classes of EEG signals (Alpha, Beta, Gamma, Delta, Theta) generated in MATLAB. Several iterations were performed to find suitable wavelet coefficients that could correctly classify all five EEG classes. Finally, the resulting parameters were implemented on an FPGA.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Review:Wavelet transform based electroencephalogram methodsijtsrd
In this paper, EEG signals are the signatures of neural activities. There have been many algorithms developed so far for processing EEG signals. The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. EEG signals are low voltage signals that are contaminated by various types of noises that are also called as artifacts. Statistical method for removing artifacts from EEG recordings through wavelet transform without considering SNR calculation is proposed Miss. N. R. Patil | Prof. S. N. Patil"Review:Wavelet transform based electroencephalogram methods" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11542.pdf http://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/11542/reviewwavelet-transform-based-electroencephalogram-methods/miss-n-r-patil
Modelling and Analysis of EEG Signals Based on Real Time Control for Wheel ChairIJTET Journal
Free versatility is center to having the capacity to perform exercises of day by day living without anyone else's input. In this proposed framework introduce an imparted control construction modeling that couples the knowledge and cravings of the client with the exactness of a controlled wheelchair. Outspread Basis Function system was utilized to characterize the predefined developments, for example, rest, forward, regressive, left and right of the wheelchair. This EEG-based cerebrum controlled wheelchair has been produced for utilization by totally incapacitated patients. The proposed outline incorporates a novel methodology for selecting ideal terminal positions, a progression of sign transforming and an interface to a controlled wheelchair.The Brain Controlled Wheelchair (BCW) is a basic automated framework intended for individuals, for example, bolted in individuals, who are not ready to utilize physical interfaces like joysticks or catches. The objective is to add to a framework usable in healing centers and homes with insignificant base alterations, which can help these individuals recover some portability. Also, it is explored whether execution in the STOP interface would be influenced amid movement, and discovered no modification with respect to the static performance.Finally, the general procedure was assessed and contrasted with other cerebrum controlled wheelchair ventures. Notwithstanding the overhead needed to choose the destination on the interface, the wheelchair is quicker than others .It permits to explore in a commonplace indoor environment inside a sensible time. Accentuation was put on client's security and comfort,the movement direction procedure guarantees smooth, protected and unsurprising route, while mental exertion and exhaustion are minimized by lessening control to destination determination.
Ecg beat classification and feature extraction using artificial neural networ...priyanka leenakhabiya
This document discusses a proposed technique for ECG beat classification and feature extraction using artificial neural networks and discrete wavelet transform. The key steps of the proposed technique include ECG data pre-processing using discrete wavelet transform to remove noise, extracting features such as RR interval and QRS complex, designing and training an artificial neural network on the extracted features, and using an Euclidean classifier to classify different ECG cases based on the minimum distance between features. Experimental results on ECG data from the MIT-BIH database show that the proposed technique achieves high classification accuracy and sensitivity compared to previous methods.
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...Md Kafiul Islam
This summarizes a document reviewing an integrated device for combined optical neuromodulation and electrical recording for chronic in-vivo applications. The device consists of an optical fiber integrated with a microelectrode array (MEA), called an optrode-MEA, that allows for simultaneous optical stimulation and electrophysiological recording. The summary is:
[1] The optrode-MEA was developed to allow for optical stimulation at one cortical site while recording neural activity from surrounding neurons to study local circuit dynamics. [2] Experiments in freely moving rats demonstrated the device could successfully record neural signals over months, mapping spatially and temporally resolved neuronal responses. [3] Analysis of recorded signals under different stimulation conditions proved the
A new eliminating EOG artifacts technique using combined decomposition method...TELKOMNIKA JOURNAL
Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
This document discusses different types of artifacts that can appear on an EEG, including how to identify and eliminate them. It separates artifacts into physiological artifacts originating from the patient's body (e.g. eye movements, muscle activity), and extraphysiological artifacts from external sources (e.g. electrodes, equipment, environment). Specific artifact types like blinks, lateral eye movements, muscle activity are described. Guidelines provided on how to reduce artifacts include closing the eyes, relaxing muscles, ensuring good electrode contact, and shielding from environmental interference.
Joint State and Parameter Estimation by Extended Kalman Filter (EKF) techniqueIJERD Editor
In order to increase power system stability and reliability during and after disturbances, power grid
global and local controllers must be developed. SCADA system provides steady and low sampling density. To
remove these limitation PMUs are being rapidly adopted worldwide. Dynamic states of power system can be
estimated using EKF. This requires field excitation as input which may not available. As a result, the EKF with
unknown inputs proposed for identifying and estimating the states and the unknown inputs of the synchronous
machine.
A Comparative Study of DOA Estimation Algorithms with Application to Tracking...sipij
Tracking the Direction of Arrival (DOA) Estimation of a moving source is an important and challenging
task in the field of navigation, RADAR, SONAR, Wireless Sensor Networks (WSNs) etc. Tracking is carried
out starting from the estimation of DOA, considering the estimated DOA as an initial value, the Kalman
Filter (KF) algorithm is used to track the moving source based on the motion model which governs the
motion of the source. This comparative study deals with analysis, significance of Non-coherent,
Narrowband DOA (Direction of Arrival) Estimation Algorithms in perception to tracking. The DOA
estimation algorithms Multiple Signal Classification (MUSIC), Root-MUSIC& Estimation of Signal
Parameters via Rotational Invariance Technique (ESPRIT) are considered for the purpose of the study, a
comparison in terms of optimality with respect to Signal to Noise Ratio (SNR), number of snapshots and
number of Antenna elements used and Computational complexity is drawn between the chosen algorithms
resulting in an optimum DOA estimate. The optimum DOA Estimate is taken as an initial value for the
Kalman filter tracking algorithm. The Kalman filter algorithm is used to track the optimum DOA Estimate.
The document discusses the extended Kalman filter (EKF), which extends the standard Kalman filter to nonlinear systems through linearization. The EKF linearizes the system equations at each time step by taking the derivative of the nonlinear functions around the current state estimate. This results in an approximate linear system that can then be processed using the standard Kalman filter equations. The key steps of the EKF algorithm are to 1) compute the linearized system matrices using derivatives, 2) use these in a first-order Taylor approximation to linearize the system equations, and 3) apply the standard Kalman filter equations to this approximate linear system to recursively estimate the state.
This project presentation summarizes a neural network approach to ECG denoising. It discusses electrocardiography and the objectives of ECG denoising such as removing powerline interference and baseline drift. The methodology involves downsampling, implementing band pass filters, differentiation, integration, squaring, thresholding, and QRS detection on the ECG signal. A feedforward neural network with backpropagation algorithm is then used for classification, where the weights are adjusted to minimize error. The activation function used is the sigmoid function. In conclusion, the neural network approach effectively detects heartbeats in an ECG signal after removing noise.
On the fractional order extended kalman filter and its application to chaotic...Mostafa Shokrian Zeini
Reference:
H. Sadeghian, H. Salarieh, A. Alasty, A. Meghdari. "On the Fractional-Order Extended Kalman Filter and its Application to Chaotic Cryptography in Noisy Environment" Applied Mathematical Modelling, 2014, 38, pp. 961-973.
This document discusses artifacts and normal variants that may appear on EEG recordings. It describes various types of artifacts that can originate from patient physiology like eye movements, muscle activity, and heartbeats. It also discusses artifacts from external interference and equipment issues. Normal variants are described like alpha rhythm, sleep transients, and frontal rhythms seen in drowsiness that should not be mistaken for epileptiform activity. The document provides details on identifying features of each artifact and variant to differentiate them from cerebral abnormalities.
Brain Training: Learning Theory, Use Patterns, Training Plans & ProtocolsSenseLabs
In this presentation Leslie Sherlin, PhD, introduces the training concepts behind Versus. You'll learn learning theory principles, training plans & protocol selection, training mechanics (EEG principles), game mechanics, as well as how Versus tracks progress & use.
This document provides information about EEG recordings and analysis. It discusses topics like the Nyquist theorem, types of EEG recordings, artifacts in EEG like EMG and eye blinks, reviewing EEG characteristics, spectral maps of EEG under different conditions, microstates in EEG, normal and abnormal distributions in EEG data, and life span normative EEG databases.
This document discusses the challenges of analyzing EEG data collected during long-term ambulatory monitoring, where many types of movement artifacts are present. It describes common artifacts like eye blinks, head movements, chewing, and electrode tapping that can interfere with seizure detection. The document proposes using time-frequency decomposition and statistical feature extraction to differentiate seizures, artifacts, and normal EEG patterns for more accurate epilepsy diagnosis and prediction. Evaluation on simulated seizure and artifact data shows the potential of this artifact removal method to improve seizure detection performance.
Removal of artifacts in EEG by averaging andNamratha Dcruz
This is a presentation on removal of artifacts in EEG by averaging and adaptive algorithms which covers a small topic in the elective Bio medical signal processing for M.Tech in Signal Processing
Kalman developed the Kalman filter in 1960-1961 to estimate the state of a dynamic system from a series of incomplete and noisy measurements. The Kalman filter uses a recursive Bayesian approach to estimate the state of a system by minimizing the mean of the squared error. It provides an efficient computational means to estimate past, present, and even future states, and can do so even when the precise nature of the modeled system is unknown.
Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...Md Kafiul Islam
This research presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 30 Hz) into account to separate artifacts from seizures. It requires a reference seizure epoch of N-sec which can either be generated from a patient-specific
seizure database (if available) or can be simulated by a simple mathematical model of seizure. The purpose of the algorithm is to reduce as much artifacts as possible without distorting the desired seizure events to be detected/diagnosed. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data:
fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently reduces the false alarms in seizure detection. Results from an extensive experiment with these datasets prove the efficacy of
the proposed algorithm and hence this algorithm (with some modifications) is expected to be a future candidate for artifact removal not only in epilepsy diagnosis applications but also in other applications (e.g. BCI or other neuroscience studies).
Artifact Detection and Removal from In-Vivo Neural SignalsMd Kafiul Islam
Background
In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest.
New method
The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed.
Results
Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio.
Comparison with existing methods
Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms.
Conclusion
Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.
This document discusses the electrocardiogram (ECG) and the electrical activity of the heart. It provides information on how ECG is used to measure heart rate and detect any heart damage. The basics of heart anatomy and function are described, including the four chambers and pacemaking nodes. The key waves of the ECG are defined, such as the P, QRS, and T waves. Methods for detecting QRS complexes are outlined, including filtering, differentiation, and thresholding. Potential artifacts in ECG signals are also reviewed, such as noise, baseline wandering, and powerline interference.
Artifacts in EEG - Recognition and differentiationRahul Kumar
This Presentation discusses the variously commonly seen artifacts in EEG, and how to recognize them. In EEG interpretation, it is often more important to identify an artifact than to identify true pathology. Once all the artifacts are ruled out, one is sure that what one is dealing with represents disease/abnormality
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...Md Kafiul Islam
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
The document discusses processing and noise cancellation of electrocardiogram (ECG) signals. It begins by explaining what an ECG is and how it is generated by the electrical activity of the heart. The ECG provides information about heart rate and the strength of the heart muscles. ECG signals are recorded using skin electrodes and contain noise from sources like power lines and electrode contact that must be removed. Common processing techniques include filtering using bandpass and adaptive filters to reduce noise and enhance the ECG waveform. Further analysis of the filtered ECG can detect heart abnormalities and conditions. Adaptive noise cancellation algorithms use a reference noise signal to minimize interference in the primary ECG input signal.
The document discusses the cerebral function monitor (CFM), also known as amplitude-integrated EEG. The CFM is a device that measures brain activity through a single lead placed on the head. It was initially developed in the 1960s for adults but was later introduced for neonates in the 1980s. The CFM can help detect seizures, monitor the effects of drugs/therapy, and aid in predicting outcomes for conditions like hypoxic-ischemic encephalopathy. It provides a simplified view of brain activity through amplitude and variability measurements. General patterns seen include normal/abnormal voltage levels and the presence/absence of sleep-wake cycling.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes a research paper that aims to estimate human consciousness levels using electroencephalography (EEG) and wavelet analysis. The paper describes collecting EEG signals from electrodes placed on a subject's forehead and earlobe. The signals are filtered to extract beta waves associated with consciousness. Wavelet decomposition is then applied using Daubechies wavelets to further analyze consciousness levels. Results found good agreement between estimated consciousness from EEG signals and a subject's actual cognitive state under different drug conditions.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
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EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
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show that an EEG alignment can be posted in a quant
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EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one‟s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.
This document describes a brainwave-controlled robotic arm. The arm is designed to help disabled individuals express themselves. Brainwaves are detected by a Neurosky headset and transmitted via Bluetooth to an Arduino microcontroller. The microcontroller maps the brainwave signals to control servo motors that move the artificial arm. Specifically, different levels of attention and meditation detected in the brainwaves will trigger opening and closing of the hand or elbow movement of the arm. The system was tested on 10 people with promising but imperfect results, suggesting it needs further development to achieve full control of the arm's movements.
IRJET- An Efficient Approach for Removal of Ocular Artifacts in EEG-Brain Com...IRJET Journal
This document summarizes a research paper that proposes a method to remove ocular artifacts from electroencephalogram (EEG) signals. Ocular artifacts are contaminants in EEG signals caused by eye blinks and movements that can distort the brain activity being measured. The proposed method uses discrete wavelet transform (DWT) to isolate the ocular artifact components in the frequency domain. It then applies adaptive noise cancellation (ANC) to the wavelet coefficients to remove the artifact components without damaging the underlying brain activity signal. The method is intended to enable more effective analysis of EEG data for applications like diagnosing epilepsy and developing brain-computer interfaces.
EEG Signal Classification using Deep Neural NetworkIRJET Journal
This document discusses using deep neural networks to classify EEG signals. It proposes using a convolutional neural network to analyze recurrence plots generated from EEG data in order to distinguish between focal and non-focal EEG signals. The recurrence plots are generated from EEG data collected from epilepsy patients. The convolutional neural network is then used to classify the recurrence plots to identify patterns associated with focal versus non-focal EEG signals. This classification of EEG signals could help doctors locate epileptic foci to inform treatment decisions.
Brain-computer interface of focus and motor imagery using wavelet and recurre...TELKOMNIKA JOURNAL
Brain-computer interface is a technology that allows operating a device without involving muscles and sound, but directly from the brain through the processed electrical signals. The technology works by capturing electrical or magnetic signals from the brain, which are then processed to obtain information contained therein. Usually, BCI uses information from electroencephalogram (EEG) signals based on various variables reviewed. This study proposed BCI to move external devices such as a drone simulator based on EEG signal information. From the EEG signal was extracted to get motor imagery (MI) and focus variable using wavelet. Then, they were classified by recurrent neural networks (RNN). In overcoming the problem of vanishing memory from RNN, was used long short-term memory (LSTM). The results showed that BCI used wavelet, and RNN can drive external devices of non-training data with an accuracy of 79.6%. The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses. Whereas in computational learning time, Adam's model is faster than AdaDelta's model.
Brain Computer Interfaces (BCIs) allow people to control electrical devices with their brain activity by recognizing patterns in brain signals. The researchers are developing a BCI using magnetoencephalography (MEG) to measure brain activity during hand movements. MEG signals are more localized than electroencephalography (EEG) signals and thus easier to classify. They aim to create a robust classifier with a low false positive rate to enable people to operate devices through thinking alone.
Denoising Techniques for EEG Signals: A ReviewIRJET Journal
The document reviews techniques for denoising EEG signals contaminated with artifacts. It discusses regression, blind source separation (BSS) including principal component analysis (PCA) and independent component analysis (ICA), wavelet transform, and empirical mode decomposition (EMD). Each method has benefits and limitations. Combining multiple current approaches can address individual constraints and provide superior outcomes than single algorithms alone by overcoming each other's limitations.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
Study and analysis of motion artifacts for ambulatory electroencephalographyIJECEIAES
Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings.
This document describes a brain-controlled robot system using EEG signals. The system uses EEG electrodes placed on the scalp to measure brain wave activity. Different patterns of brain waves can be translated into commands to control a mobile robot in real time. The goal is to develop a robot that can assist disabled people and allow them to move independently without physical movement. The system works by analyzing EEG signals through techniques like fast Fourier transforms to separate different brain wave frequencies associated with different mental states and intentions. This allows the user to think of commands to direct the robot's movement. The system aims to improve quality of life for people with disabilities.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single
channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined
commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured
EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian
mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is
asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed
that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a
certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using
2400 test EEG samples recorded from 10 subjects.
METHODS OF COMMAND RECOGNITION USING SINGLE-CHANNEL EEGSijistjournal
This work proposes to recognize a user's commands by analysing his/her brainwaves captured with single channel electroencephalogram (EEG). Whenever a user intends to issue one of the pre-defined commands, the proposed system prompts him/her all the candidate commands in turn. Then, the user is asked to be concentrated as possible as he/she can, when the desired command is shown. It is assumed that the concentration will present a certain pattern of “Yes” in the captured EEG, as opposed to a certain pattern of “No” when the user is relaxed. Accordingly, the task is to determine that the captured EEG is “Yes” or not. This work compares three recognition methods, respectively, based on Gaussian mixture models, hidden Markov models and recurrent neural network, and conducts experiments using 2400 test EEG samples recorded from 10 subjects.
Similar to Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger (20)
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Your Skill Boost Masterclass: Strategies for Effective Upskilling
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA Merger
1. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 80
Performance Comparison of Known ICA Algorithms to a
Wavelet-ICA Merger
Janett Walters-Williams jwalters@utech.edu.jm
School of Computing & Information Technology
Faculty of Engineering & Computing
University of Technology, Jamaica
237 Old Hope Road, Kingston 6, Jamaica W.I.
Yan Li liyan@usq.edu.au
Department of Mathematics & Computing
Faculty of Sciences
University of Southern Queensland
Toowoomba, Australia
Abstract
These signals are however contaminated with artifacts which must be removed to have pure EEG
signals. These artifacts can be removed by Independent Component Analysis (ICA). In this paper
we studied the performance of three ICA algorithms (FastICA, JADE, and Radical) as well as our
newly developed ICA technique. Comparing these ICA algorithms, it is observed that our new
techniques perform as well as these algorithms at denoising EEG signals.
Keywords: Independent Component Analysis, Wavelet Transform, Unscented Kalman Filter,
Electroencephalogram
1. INTRODUCTION
The use of Electroencephalogram in the field of Medicine has had a great impact on the study of
the human brain. The signals received have several origins however that lead to the complexity of
their identification. This complexity is made of both the pure EEG signal and other non-cerebral
signals called artifacts or noise. The artifacts have resulted in the contamination of the EEG
signals, hence the removal of these artifacts has generated a large number of denoising
techniques.
One method has been Independent Component Analysis (ICA) originating from the field of Blind
Source Separation [5]. This technique calls for the separation of the EEG into its constituent
independent components (ICs) and then eliminating the ICs that are believed to contribute to the
artifact sources. It is subjective, inconvenient and a time consuming process when dealing with
large amount of EEG data. Another method employed is wavelet transformation. This technique
calls for the decomposition of the EEG signals into wavelets and artifacts removal done using
thresholding and shrinkage.
Each of the above techniques presents their own limitations. In our opinion a combination of the
two should produce a more effective technique. This is possible as each technique is used to
overcome the limitation of the other. We present in this paper therefore a new method of
extracting artifacts from EEG signals – Cycle Spinning Wavelet Transform ICA (CTICA). CTICA is
compared to other known ICA algorithms, and saving useful EEG data.
2. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 81
2. SUPPORTING LITERATURE
2.1 EEG Signals
The language of communication with the nervous system is electric so when the neurons of the
human brain process information, they do so by changing the flow of electrical currents across
their membranes. These changing currents generate electric and magnetic fields that can be
recorded from the surface of the scalp. The electric fields are measured by attaching small
electrodes to the scalp. The potentials between different electrodes are then amplified and
recorded as the electroencephalogram; (EEG), which means the writing out of the electrical
activity of the brain (that which is inside the head). EEG recordings therefore, show the overall
activity of the millions of neurons in the brain.
There are five basic wave types, measured in Hertz (HZ), found in EEG signals (Tab. 1). The
most prominent type is the alpha rhythm recorded mainly over the posterior regions of the scalp
close to the places in the brain that process visual information. When the eyes are open the alpha
rhythm is very small and when the eyes are closed it becomes large.
Type Frequency(Hz) Normally
Delta (δ) 0.5-4 Hz
Deep, dreamless sleep, non-REM
sleep, unconscious
Theta (θ) 4 – 8 Hz
Intuitive, creative, recall, fantasy,
imaginary, dream
Alpha (α) 8 – 13 Hz
Relaxed, but not drowsy, tranquil,
conscious
Beta (β) 13 – 30 Hz
Formerly SMR, relaxed yet
focused, integrated, Thinking,
aware of self & surroundings,
Alertness, agitation
Gamma (γ) 30 – 100+ Hz
Motor Functions, higher mental
activity
TABLE 1: Wave Types Found in EEG Signals (adapted from Neurosky Inc. 2009 Brain Wave Signal (EEG)
of NeuroSky, Inc.)
Since an EEG is used to analyzed brain function it is used in clinical practice to:
(i) Diagnose epilepsy and see what type of seizures is occurring. EEG is the most useful
and important test in confirming a diagnosis of epilepsy.
(ii) Check for problems with loss of consciousness or dementia.
(iii) Help find out a person's chance of recovery after a change in consciousness.
(iv) Find out if a person who is in a coma is brain-dead.
(v) Study sleep disorders, such as narcolepsy.
(vi) Watch brain activity while a person is receiving general anesthesia during brain surgery.
(vii)Help find out if a person has a physical problem (problems in the brain, spinal cord, or
nervous system) or a mental health problem.
Being a physical system however, EEG is subjected to random disturbance. The measurements
or observations are generally contaminated with other non-cerebral signals called artifacts or
noise caused by the electronic and mechanical components of the measuring devices. These
may include EOG (Eye-induced) artifacts (includes eye blinks and eye movements); EKG (Fig 1)
(cardiac) artifacts; EMG (muscle activation)-induced artifacts; and Glossokinetic (chewing &
sucking movement) artifacts. Artifacts sometimes mimic EEG signals and overlay these signals
resulting in distortion making analysis impossible. In clinical practice areas in the reading with
artifacts are cancelled resulting in considerable information loss, thus sometimes resulting in
misdiagnosis.
3. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 82
FIGURE 1: EEG Signal corrupted with ECG/EKG and line signals (adapted from Artifact Removal from EEG
Signals using Adaptive Filters In Cascade, A Garcés Correa et al, Journal of Physics: Conference Series 90,
2007)
Artifacts must be eliminated or attenuated to ensure correct analysis and diagnosis. Through the
years there have been different methods of denoising such as artifacts rejection, regression and
Principal Components Analysis (PCA). More recently two other methods have been discussed –
Independent Component Analysis (ICA) and Wavelet Transform (WT).
2.2 Independent Component Analysis
When a signal is contaminated it is a combination of the true signal S(t) and the artifacts ε(t)
producing equation (1) where c(t) is the contaminated signal.
( ) ( ) ( )c t S t tε= +
(1)
Researchers have been utilizing ICA to remove ε(t).
ICA is an extension of PCA which originated from the field of Blind Source Separation. It is
suitable for performing source separation where
(i) sources are independent
(ii) propagation delays of mixing medium are negligible
(iii) source are analog with pdfs not too unlike the gradient of a logistic sigmoid
(iv) the number of independent signals sources is the same as the number of sensors.
Investigations show that EEG satisfies (i) since there are statistically independent brain
processes, (ii) since the volume conduction in the brain tissue is efficiently instantaneous. The
assumption of (iii) is plausible but the assumption that EEG signals are a linear mixture of exactly
N sources is questionable since we are do not know the effective number of statistically
independent brain signals contributing to the EEG recorded from the scalp [19]. ICA can
therefore be used to performance separations on these signals. There are problems with using
ICA however
(i) Its performance depends however on the length of the dataset, because the larger
the set the more likely person will have to deal with an over complete ICA which
cannot separate artifacts from the signals.
(ii) When ICA performs separations sometimes some useful signals maybe removed as
a part of the artifacts resulting in information loss [11].
4. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 83
2.3 Wavelet Transform
Wavelet analysis, a sub brand of applied mathematics has been used to decompose signals in
the time frequency scale plane (fig 2). It has been found to be an efficient technique for non-
stationary signal processing of which EEG falls. [1] [22]. Its capability to transform the EEG time
domain signal into time and frequency localization helps researchers understand more the
behaviour of the signals.
FIGURE 2: Demonstration of (a) a wave and (b) a wavelet. Notice that the wave has an easily discernible
frequency while the wavelet has a pseudo frequency in that the frequency varies slightly over the length of
the wavelet. (adapted from D.L. Fugal. 2009. Conceptual Wavelets in Digital Signal Processing: An in depth
Practical Approach for the Non-Mathematician, Space & Signals Technologies LLC)
There are two basic types of wavelet transform. One type of wavelet transform is designed to be
easily reversible (invertible); that means the original signal can be easily recovered after it has
been transformed. This kind of wavelet transform is used for image compression and cleaning
(noise and blur reduction). Typically, the wavelet transform of the image is first computed, the
wavelet representation is then modified appropriately, and then the wavelet transform is reversed
(inverted) to obtain a new image.
The second type of wavelet transform is designed for signal analysis for study of EEG or other
biomedical signals. In these cases, a modified form of the original signal is not needed and the
wavelet transform need not be inverted (it can be done in principle, but requires a lot of
computation time in comparison with the first type of wavelet transform). Decomposition- into
wavelets is done by a “mother and “father” wavelet function. These “mother” functions include
Haar, Daubechies and Mexican Hat. Equation (2) shows that it is possible to build a wavelet for
any function by dilating the mother wavelet function ψ(t) with a coefficient 2
j
, and translating the
resulting function on a grid whose interval is proportional to 2
–j
.
2
( , ) ( ) 2 ( 2 )
a
a
a b t t bψΨ = −
(2)
Compressed versions of the wavelet function match the high-frequency components, while
stretched versions match the low-frequency components. By correlating the original signal with
wavelet functions of different sizes, the details of the signal can be obtained at several scales or
moments. These correlations with the different wavelet functions can be arranged in a
hierarchical scheme called multi-resolution decomposition. The multi-resolution decomposition
algorithm separates the signal into “details” at different moments and wavelet coefficients [22]
[23]. These coefficients are called the Discrete Wavelet Transform (DWT) of the signal. As the
moments increase the amplitude of the discrete details become smaller however the coefficients
of the useful signals increase [27] [28].
If the details are small enough they might be omitted without substantially affecting the main
signals. This omission is done through Thresholding. There are two main ways to denoise a
signal in WT – soft and hard thresholding. Research as shown that soft-thresholding has better
mathematical characteristics [27] [28] and provides smoother results [9]
5. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 84
2.4 Unscented Kalman Filter
Unscented Kalman Filter (UKF) is a Bayesian filter which uses minimum mean-squared error
(MMSE) as the criterion to measure optimality [4][34]. For highly nonlinear systems, the linear
estimate of the nonlinear model does not provide a good approximation of the model, and the
Extended Kalman Filter (EKF) will not track signals around sharp turning points. Another problem
with the EKF is that the estimated covariance matrix tends to underestimate the true covariance
matrix and therefore risks becoming inconsistent in the statistical sense without the addition of
"stabilising noise". UKF was found to address these flaws. It involves the Unscented
Transformation (UT), a method used to calculate the first and second order statistics of the
outputs of nonlinear systems with Gaussian. The nonlinear stochastic system used for the
algorithm is:
1k k k k
k k k
x A x B u v
y H x w
+ = + +
= +
(3)
where A and H are the known and constant matrices respectively, xk is the unobserved state of
the system, uk is a known exogenous input, yk is the observed measurement signal, vk is the
process noise and wk is the measurement noise.
FIGURE 3: Noisy EEG and its Wavelet Transform at different scales (adapted from Weidong Z.,
Yingyuan, L. 2001. EEG Multi-resolution Analysis using Wavelet Transform, 23rd
Annual
International Conference of the IEEE Engineering in Medicine and Biology Society
(IEEE/EMBS) 2001)
UKF uses the intuition that it is easier to approximate a probability distribution function rather than
to approximate an arbitrary nonlinear function or transformation. Following this intuition, a set of
sample points, called sigma points, are generated around the mean, which are thenpropagated
through the nonlinear map to get a more accurate estimation of the mean and covariance of the
mapping results. In this way, it avoids the need to calculate the Jacobian, which for complex
functions can be a difficult task in itself (i.e., requiring complicated derivatives if done analytically
or being computationally costly if done numerically).
3. PREVIOUS RESEARCH
WT and ICA in recent years have often been used in Signal Processing. [22] [27]. Although ICA is
popular and for the most part does not result in much data loss; its performance depends on the
size of the data set i.e. the number of signals. The larger the set, the higher the probability that
6. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 85
the effective number of sources will overcome the number of channels (fixed over time), resulting
in an over complete ICA. This algorithm might not be able to separate noise from the signals.
Another problem with ICA algorithms has to do with the signals in frequency domain. Although
noise has different distinguishing features, once they overlap the EEG signals ICA cannot filter
them without discarding the true signals as well. This results in data loss.
WT utilizes the distinguishing features of the noise however. Once wavelet coefficients are
created, noise can be identified. Decomposition is done at different levels (L); DWT produces
different scale effects (Fig 3). Weidong et al. [25] proved that as scales increase the WT of EEG
and noise present different inclination. Noise concentrates on scale 21, decreasing significantly
when the scale increases, while EEG concentrates on the 22-25 scales. Elimination of the smaller
scales denoise the EEG signals. WT therefore removes any overlapping of noise and EEG
signals that ICA cannot filter out.
More recently there has been research comparing the denoising techniques of both. It was found
(i) If artifacts and signals are nearly the same or higher amplitude, wavelets had difficultly
distinguishing them. ICA on the other hand looks at the underlying distributions thus
distinguishing each [29].
(ii) ICA gives high performance when datasets are large. It suffers from the trade off between a
small data set and high performance [11].
Research therefore shows that ICA and wavelets complement each other, removing the
limitations of each [29]. Since then research as been done applying a combination of both with
ICA as a per- or post- denoising tool. Inuso et al. [11] used them where ICA and wavelets are
joint. They found that their method outperformed the pre- and post- ICA models.
4. RESEARCH DATASETS
EEG data was taken from two sites
(i) http://sccn.ucsd.edu/~arno/fam2data/publicly_available_EEG_data.html. The signals
from here are contaminated with EOG. Data is sampled at a rate of 128 samples per
second recorded from 32 electrodes at 1000Hz
(ii) http://www.filewatcher.com/b/ftp/ftp.ieee.org/uploads/press/rangayyan.0.0.html. Data was
collected at a sampling rate of 1000Hz but noise free. These signals had to artificially
contaminated
These two sites produce signals of different sizes as well as 1D and 2D signals.
5. METHODOLOGY
When a signal is decomposed it is represented as a set of wavelet coefficients that correlates to
high frequency sub-bands. Artifacts are usually of low frequency and can be removed by
shrinkage or thresholding. Research has shown however that thresholding has a slow response
[22] [23]. In this paper we are presently another method to denoising using WT and ICA. Some of
the ideas appear in earlier algorithms however the main difference of CTICA is the use of cycle
spinning; the merger of Wavelet Transform and ICA into one and the improvement of denoising.
The presented method is based on decomposition by using Symmlets which is a near symmetric
extension of Daubechies. Symlets are orthogonal and its regularity increases with the increase in
the number of moments [6]. After experiments the number of vanishing moments chosen is 8
(Sym8).
7. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 86
FIGURE 4: Proposed Artifacts Removal System
A block diagram representation of the proposed work is shown in FIGURE 4. EEGs are acquired
and Cycle Spinning applied. Cycle Spinning utilizes the periodic time invariance of the wavelet
transform to separate noise from signals. The EEG signals are then decomposed by Forward
DWT using the Symmlet family of wavelets. The wavelet coefficients are separated into
statistically independent sources using ICA and denoising takes place. Each IC is then filtered
using UKF. Finally, the sources that are identified as non-artifacts are used to reconstruct the
artifact-free EEGs and Cycle Spinning applied again.
6. RESULTS & DISCUSSION
We conducted experiments, using the above mentioned signals, in Matlab 7.8.0 (R2009) on a
laptop with AMD Athlon 64x2 Dual-core Processor 1.80GHz. Noisy signals were generated by
adding noise to the original noise-free signals and the length of all signals, N, were truncated to
lengths of power of twos i.e. 2
x
.
FastICA Jade Radical CT-ICA
4.1954 4.1909 4.1865 4.1912
7.1276 7.1191 7.1106 7.1192
5.1226 5.1281 5.1226 5.1278
8.0569 8.0484 8.0399 8.0487
7.8736 7.8827 7.8736 7.8815
3.5646 3.5696 3.5646 3.5703
6.0995 6.1057 6.0995 6.1042
2.733 2.7364 2.733 2.7361
0.1374 0.1373 0.1372 0.1373
8.658 8.6521 8.6462 8.6499
0.284 0.2841 0.284 0.2841
0.2234 0.2235 0.2234 0.2235
3.2436 3.2395 3.2355 3.2387
TABLE 2: MSE for 13 EEG signals (x.xe+07)
6.1 Testing Against Known ICA Algorithms
We compared the performance of our method with several state-of-the art ICA algorithms -
FastICA, Radical, and Jade. All the algorithms were downloaded from the web sites of the
respective authors. In the case of FastICA a symmetrical view based on the tan score function
was used for comparison. To determine the quality of each signal the Mean Square Error (MSE),
the Peak Signal to Noise Ratio (PSNR), the Signal to Distortion Ratio (SDR), the Signal to noise
Ratio (SNR) and the Amari Performance Index were calculated.
ICA
Denoising
Decomposed
into Wavelets
Raw
EEG
Filtering
using UKF
Reconstruct
Signal
Independent
Components
Wavelets
Pure
EEG
8. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 87
MSE measures the average of the square of the "error" and defined as:
2
1 1
1
[ ( , ) '( , )]
M N
y x
MSE I x y I x y
MN = =
= −∑ ∑
(4)
The error is the amount by which the estimator differs from the quantity to be estimated. The
difference occurs because of randomness or because the estimator doesn't account for
information that could produce a more accurate estimate. TABLE 2 shows the MSE for 13
signals. Observations show that there is not much difference in the MSE for each algorithm. The
lower the MSE the lesser the error on the signal; it can seen that on average our method
performed better than FastICA and Jade. Radical had a lower MSE.
FastICA Jade Radical CT-ICA
-18.0969 -18.0923 -18.0877 -18.0926
-20.3987 -20.3935 -20.3883 -20.3935
-18.9641 -18.9687 -18.9641 -18.9685
-20.9309 -20.9263 -20.9217 -20.9265
-20.8309 -20.836 -20.8309 -20.8353
-17.3893 -17.3954 -17.3893 -17.3962
-19.7221 -19.7266 -19.7221 -19.7255
-16.2355 -16.241 -16.2355 -16.2405
-23.2477 -23.2453 -23.243 -23.2442
-21.2434 -21.2404 -21.2375 -21.2393
-26.4025 -26.4042 -26.4025 -26.404
-25.359 -25.3609 -25.359 -25.3611
-16.9794 -16.974 -16.9686 -16.9729
TABLE 3: PSNR for 13 EEG signals
PSNR is the ratio between the maximum possible power of a signal and the power of corrupting
noise that affects the fidelity of its representation. It is defined as:
2
1010 log ( )
MAX
PSNR
MSE
= ×
(5)
Because many signals have a very wide dynamic range, PSNR is usually expressed in terms of
the logarithmic decibel scale. In this research MAX takes the value of 255. Tab 3 shows the
PSNR for 13 signals. If the PSNR is high then the ratio of signal to noise is higher and therefore
the algorithm is considered good.
After experiments it can be seen that our algorithm has the same PSNR on average. It was also
seen that it has a higher PSNR than Jade and Radical. The similar signal to noise ratio can be
seen in the SNR graph in figure 5 where only Jade has a different value.
9. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 88
FIGURE 5: SNR results for 13 signals
The accuracy of the separation for each algorithm in terms of the signals can be calculated by the
total SDR defined as:
( )
2
1
2
1
( )
( , ) 1,...
( ) ( )
L
i
n
i i L
i i
n
x n
SD R x y i m
y n x n
=
=
= =
−
∑
∑ (6)
where xi(n) is the original source signal and yi(n) is the reconstructed signal. When SDR are
calculated any found below 8-10dB are considered to fail separation. Fig 5 shows that all four
algorithms had SDR above 8dB. It also shows that CTICA had SDR very close to the other four
so that there was no differentiation in the graph.
FIGURE 6: SDR results for 13 signals
10. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 89
The global accuracy of the separation of each algorithm was tested using the Amari performance
index defined as:
, 1
| | | |1
1
2 max | | max | |
m
ij ij
err
i j k ik k kj
p p
P
m p p=
= + −
∑
(7)
where pij = (BA)ij. It assesses the quality of the de-mixing matrix W for separating observations
generated by the mixing matrix A. The lower the Amari index, the more accurate the separation
is. We have normalized all values of the Amari index to be between 0 and 1 (the max). The Amari
indexes obtained for the different algorithms and for different sample sizes are presented in
TABLE 4. Observations show that the Amari indexes for our method is very similar to those of
Jade and FastICA. On average however it has a lower Amari than both FastICA and Jade but not
Radical.
FastICA Jade Radical CT-ICA
1238 1237 1236 1237
1583 1582 1581 1582
1363 1363 1363 1363
1669 1668 1667 1668
1652 1653 1652 1653
1140 1141 1140 1141
1477 1478 1477 1478
989 990 989 990
2069 2068 2068 2068
1720 1720 1719 1720
2683 2683 2683 2683
2471 2471 2471 2471
1085 1085 1084 1084
TABLE 4: Amari Test Results for 13 EEG signals (x.xe-05)
6.2 Testing against Known WT-influenced Algorithms
Zhou et al. [28] in 2004 found that a combination of wavelet threshold de-noising and ICA
resulted in the removal of electromyogram (EMG) and electrocardiograph (ECG) artifacts from
EEG signals. Further research in 2007 by Inuso et al. [11] resulted in the creation of a new
technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent
Component Analysis (WICA). After comparison to pre- and post- ICA and wavelet denoising
using artificial artifact-laden EEG datasets they found that this combination had the best artifact
separation performance for every kind of artifact also allowing for the minimum information loss.
These show that a merger of WT and ICA is more effective.
Pre-WT Post-WT WT-UKF WT-ICA CT-ICA
33.9443 1.1158e3 1.1025 1.1051 1.0947
29.0936 1.0438 1.0499 1.0379 1.0372
23.9498 1.0058 997.4019 982.4991 979.2423
TABLE 5: Sample MSE for 3 EEG signals
Sameni et al. [21] experimented with denoising using EKF on ECG data. They found that the
results show that the EKF may be used as a powerful tool for the extraction of the ECG signals
from noisy measurements. Jacob and Martin [12] tested a combination of WT and Weiner Filter.
They concluded that this combination basic denoising using only WT
11. Janett Walters-Williams & Yan Li
Signal Processing: An International Journal, Volume (5) : Issue (3) : 2011 90
Pre-WT Post-WT WT-UKF WT-ICA CT-ICA
32.8231 17.655 17.7071 17.6966 17.7377
33.4928 17.9446 17.9192 17.9693 17.9722
34.3378 18.1058 18.1421 18.2075 18.2219
TABLE 6: Sample PSNR for 3 EEG signals
As stated before ICA and WT complement each other, removing the limitations of each [29];
researchers have shown that the combination of WT and ICA is more effective than ICA or WT
alone supporting this theory. They have also shown that the performance of WT improves with
the addition of Filters. In our research investigations have shown that when compared to the
post- and pre- ICA models, a combination of WT with (i) ICA, or (ii) UKF we have found as seen
in Tables 5 and 6 that the merger of all three outperformed all except the Pre-ICA model. This
conforms to the findings of researchers.
7. CONCLUSION
In recent years researchers have used both ICA algorithms and WT to denoise EEG signals. In
this paper we propose a new method – Cycle Spinning Wavelet Transform ICA (CTICA). From
the experiments we can conclude the following for CTICA
(I) It can be seen from the experiments that it can successfully separate noise from EEG
signals.
(II) It has outperformed FastICA and JADE as far as MSE was concerned,
(III) It has outperformed JADE and Radical with PSNR.
(IV) It has the similar in SDR and Amari index
(V) It outperforms different WT model designs except for the Pre-ICA model.
Based on these results it can be concluded that CTICA has an overall performance which is
better than all three ICA algorithms and most WT model, i.e. it is the most consistent and robust
denoising method.
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