International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
IRJET-Analysis of EEG Signals and Biomedical Changes due to Meditation on Bra...IRJET Journal
This document reviews research on analyzing EEG signals and biomedical changes due to meditation on the brain. It begins with an abstract stating that meditation can significantly contribute to physical and mental relaxation and is gaining popularity as a stress reduction technique. The review examines the effects of meditation on the human brain using electroencephalography (EEG) signals and various signal processing methods. It summarizes several studies that have found increases in theta and alpha band power and decreases in overall frequency during meditation based on EEG analyses. The document provides background on EEG and different brainwave frequencies and then proposes a system model for analyzing meditation effects on brainwaves through feature extraction from EEG data and classification of meditational vs. non-meditational states.
Distinguishing Cognitive Tasks Using Statistical Analysis TechniquesIOSR Journals
- The document discusses distinguishing between cognitive tasks like mathematics, physics, and chemistry using EEG signal analysis and statistical techniques.
- EEG signals were recorded from students performing different cognitive tasks and analyzed using Kruskal-Wallis statistical testing.
- The results of the Kruskal-Wallis test showed a significant difference between the EEG signals corresponding to the different cognitive tasks.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
Overview of Machine Learning and Deep Learning Methods in Brain Computer Inte...IJCSES Journal
Research under the field of Brain Computer Interfaces is adapting various Machine Learning and Deep
Learning techniques in recent times. With the advent of modern BCI, the data generated by various devices
is now capable of detecting brain signals more accurately. This paper gives an overview of all the steps
involved in the process of applying Machine Learning as well as Deep Learning methods from Data
Acquisition to application of algorithms. It aims to study techniques currently employed to extract data,
features from brain data, different algorithms employed to draw insights from the extracted features, and
how it can be used in various BCI applications. By this study, I aim to put forward current Machine
Learning and Deep Learning Trends in the field of BCI.
IRJET- Review on Depression Prediction using Different MethodsIRJET Journal
This document summarizes various methods that have been used to predict depression. It discusses using questionnaires and psychometric tests administered by psychiatrists, analyzing EEG signals through signal processing techniques, and using artificial intelligence and machine learning algorithms to analyze text, audio, and visual inputs. Specifically, it describes using standardized tests like the Hospital Anxiety and Depression Scale and Beck's Depression Inventory, extracting features from EEG frequency bands to classify subjects, and employing sentiment analysis and other text analysis on speech, facial expressions, and head movements to predict mental states. The document provides background on relevant concepts in artificial intelligence, machine learning, deep learning, and neural networks.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
IRJET-Analysis of EEG Signals and Biomedical Changes due to Meditation on Bra...IRJET Journal
This document reviews research on analyzing EEG signals and biomedical changes due to meditation on the brain. It begins with an abstract stating that meditation can significantly contribute to physical and mental relaxation and is gaining popularity as a stress reduction technique. The review examines the effects of meditation on the human brain using electroencephalography (EEG) signals and various signal processing methods. It summarizes several studies that have found increases in theta and alpha band power and decreases in overall frequency during meditation based on EEG analyses. The document provides background on EEG and different brainwave frequencies and then proposes a system model for analyzing meditation effects on brainwaves through feature extraction from EEG data and classification of meditational vs. non-meditational states.
Distinguishing Cognitive Tasks Using Statistical Analysis TechniquesIOSR Journals
- The document discusses distinguishing between cognitive tasks like mathematics, physics, and chemistry using EEG signal analysis and statistical techniques.
- EEG signals were recorded from students performing different cognitive tasks and analyzed using Kruskal-Wallis statistical testing.
- The results of the Kruskal-Wallis test showed a significant difference between the EEG signals corresponding to the different cognitive tasks.
Recognition of emotional states using EEG signals based on time-frequency ana...IJECEIAES
The recognition of emotions is a vast significance and a high developing field of research in the recent years. The applications of emotion recognition have left an exceptional mark in various fields including education and research. Traditional approaches used facial expressions or voice intonation to detect emotions, however, facial gestures and spoken language can lead to biased and ambiguous results. This is why, researchers have started to use electroencephalogram (EEG) technique which is well defined method for emotion recognition. Some approaches used standard and pre-defined methods of the signal processing area and some worked with either fewer channels or fewer subjects to record EEG signals for their research. This paper proposed an emotion detection method based on time-frequency domain statistical features. Box-and-whisker plot is used to select the optimal features, which are later feed to SVM classifier for training and testing the DEAP dataset, where 32 participants with different gender and age groups are considered. The experimental results show that the proposed method exhibits 92.36% accuracy for our tested dataset. In addition, the proposed method outperforms than the state-of-art methods by exhibiting higher accuracy.
Overview of Machine Learning and Deep Learning Methods in Brain Computer Inte...IJCSES Journal
Research under the field of Brain Computer Interfaces is adapting various Machine Learning and Deep
Learning techniques in recent times. With the advent of modern BCI, the data generated by various devices
is now capable of detecting brain signals more accurately. This paper gives an overview of all the steps
involved in the process of applying Machine Learning as well as Deep Learning methods from Data
Acquisition to application of algorithms. It aims to study techniques currently employed to extract data,
features from brain data, different algorithms employed to draw insights from the extracted features, and
how it can be used in various BCI applications. By this study, I aim to put forward current Machine
Learning and Deep Learning Trends in the field of BCI.
IRJET- Review on Depression Prediction using Different MethodsIRJET Journal
This document summarizes various methods that have been used to predict depression. It discusses using questionnaires and psychometric tests administered by psychiatrists, analyzing EEG signals through signal processing techniques, and using artificial intelligence and machine learning algorithms to analyze text, audio, and visual inputs. Specifically, it describes using standardized tests like the Hospital Anxiety and Depression Scale and Beck's Depression Inventory, extracting features from EEG frequency bands to classify subjects, and employing sentiment analysis and other text analysis on speech, facial expressions, and head movements to predict mental states. The document provides background on relevant concepts in artificial intelligence, machine learning, deep learning, and neural networks.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
IRJET- Deep Learning Technique for Feature Classification of Eeg to Acces...IRJET Journal
This document provides a survey of research on using deep learning techniques to classify EEG signals in order to detect mental status, specifically depression. It summarizes 11 previous studies that used methods like convolutional neural networks, support vector machines, and case-based reasoning to analyze EEG features and classify subjects as depressed or healthy. Classification accuracies ranged from 81-98%. The document concludes that advances in deep learning and increased EEG data availability have led to improved detection of depression from EEG signals.
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.
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
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.
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
IRJET- Depression Prediction System using Different MethodsIRJET Journal
This document summarizes a research paper that proposes a depression prediction system using different methods. The system would use three approaches: a question and answer part using standardized depression questionnaires; EEG signal processing to analyze brain activity; and sentiment analysis of social media posts. Machine learning algorithms like neural networks and naive Bayes would be used for classification. The goal is to help predict depression early through an online system that could be used by doctors and individuals. Key areas discussed include artificial intelligence, machine learning techniques for classification like support vector machines and logistic regression, and prior research analyzing EEG signals and social media posts to predict depression.
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%.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
Analysis of emotion disorders based on EEG signals ofHuman BrainIJCSEA Journal
In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.
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.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
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.
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET- Early Stage Prediction of Parkinson’s Disease using Neural NetworkIRJET Journal
This document describes a study that uses artificial neural networks and deep neural networks to predict Parkinson's disease at early stages. The researchers analyze speech and motor data from Parkinson's patients. They build ANN and DNN models to classify whether patients have Parkinson's or not. The ANN achieved 80-90% accuracy on the test data, while the DNN was able to predict Parkinson's with over 81% accuracy, outperforming other common machine learning algorithms. The study aims to help diagnose Parkinson's earlier to improve treatment outcomes for patients.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
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.
This document summarizes a research paper that proposes using EEG signals for person identification. It describes collecting EEG data from subjects using electrodes placed on the scalp. Wavelet packet decomposition is used to extract features from the EEG signals, focusing on the alpha frequency band between 8-12 Hz. Learning vector quantization is then used to classify the EEG patterns and identify individuals. The methodology involves preprocessing the EEG data, extracting features using wavelet packet decomposition, and classifying the features with LVQ to identify persons based on their unique EEG signatures.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
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
Prediction Model for Emotion Recognition Using EEGIRJET Journal
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
IRJET- Deep Learning Technique for Feature Classification of Eeg to Acces...IRJET Journal
This document provides a survey of research on using deep learning techniques to classify EEG signals in order to detect mental status, specifically depression. It summarizes 11 previous studies that used methods like convolutional neural networks, support vector machines, and case-based reasoning to analyze EEG features and classify subjects as depressed or healthy. Classification accuracies ranged from 81-98%. The document concludes that advances in deep learning and increased EEG data availability have led to improved detection of depression from EEG signals.
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.
IRJET- Precision of Lead-Point with Support Vector Machine based Microelectro...IRJET Journal
This document discusses using microelectrode recordings (MER) with support vector machine learning to study the function of subthalamic nucleus (STN) neurons in the human brain during deep brain stimulation for Parkinson's disease. The study aims to improve the signal-to-noise ratio of MER signals and precisely identify the location of the STN to ensure safety and efficacy of deep brain stimulation chip implantation. MER is found to confirm the presence of abnormal STN neurons and clear positioning of the microchip electrode in the target area. Access to functional data from neurons deep in the brain using MER may help further elucidate cryptic aspects of brain function.
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.
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
IRJET- Depression Prediction System using Different MethodsIRJET Journal
This document summarizes a research paper that proposes a depression prediction system using different methods. The system would use three approaches: a question and answer part using standardized depression questionnaires; EEG signal processing to analyze brain activity; and sentiment analysis of social media posts. Machine learning algorithms like neural networks and naive Bayes would be used for classification. The goal is to help predict depression early through an online system that could be used by doctors and individuals. Key areas discussed include artificial intelligence, machine learning techniques for classification like support vector machines and logistic regression, and prior research analyzing EEG signals and social media posts to predict depression.
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%.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Robot Motion Control Using the Emotiv EPOC EEG SystemjournalBEEI
Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.
Analysis of emotion disorders based on EEG signals ofHuman BrainIJCSEA Journal
In this research, the emotions and the patterns of EEG signals of human brain are studied. The aim of this research is to study the analysis of the changes in the brain signals in the domain of different emotions. The observations can be analysed for its utility in the diagnosis of psychosomatic disorders like anxiety and depression in economical way with higher precision.
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.
Classification of emotions induced by horror and relaxing movies using single-...IJECEIAES
It has been observed from recent studies that corticolimbic Theta rhythm from EEG recordings perceived as fear or threatening scene during neural processing of visual stimuli. In additions, neural oscillations’ patterns in Theta, Alpha and Beta sub-bands also play important role in brain’s emotional processing. Inspired from these findings, in this paper we attempt to classify two different emotional states by analyzing single-channel EEG recordings. A video clip that can evoke 3 different emotional states: neutral, relaxation and scary is shown to 19 college-aged subjects and they were asked to score their emotional outcome by giving a number between 0 to 10 (where 0 means not scary at all and 10 means the most scary). First, recorded EEG data were preprocessed by stationary wavelet transform (SWT) based artifact removal algorithm. Then power distribution in simultaneous time-frequency domain was analyzed using short-time Fourier transform (STFT) followed by calculating the average power during each 0.2s time-segment for each brain sub-band. Finally, 46 features, as the mean power of frequency bands between 4 and 50 Hz during each time-segment, containing 689 instances—for each subject —were collected for classification. We found that relaxation and fear emotions evoked during watching scary and relaxing movies can be classified with average classification rate of 94.208% using K-NN by applying methods and materials proposed in this paper. We also classified the dataset using SVM and we found out that K-NN classifier (when k = 1) outperforms SVM in classifying EEG dynamics induced by horror and relaxing movies, however, for K > 1 in K-NN, SVM has better average classification rate.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
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.
Epileptic Seizure Detection using An EEG SensorIRJET Journal
This document presents a method for detecting epileptic seizures using an EEG sensor and signal processing techniques. It involves using an EEG headset to record raw brain wave data, filtering the signals to remove noise, applying discrete wavelet transform to extract features from different frequency bands, and using a support vector machine classifier to classify segments as normal, interictal, or ictal based on the extracted features. The proposed method aims to help doctors more accurately diagnose and monitor epilepsy in patients by objectively detecting seizures from EEG data in near real-time.
IRJET- Early Stage Prediction of Parkinson’s Disease using Neural NetworkIRJET Journal
This document describes a study that uses artificial neural networks and deep neural networks to predict Parkinson's disease at early stages. The researchers analyze speech and motor data from Parkinson's patients. They build ANN and DNN models to classify whether patients have Parkinson's or not. The ANN achieved 80-90% accuracy on the test data, while the DNN was able to predict Parkinson's with over 81% accuracy, outperforming other common machine learning algorithms. The study aims to help diagnose Parkinson's earlier to improve treatment outcomes for patients.
Improved feature exctraction process to detect seizure using CHBMIT-dataset ...IJECEIAES
One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.
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.
This document summarizes a research paper that proposes using EEG signals for person identification. It describes collecting EEG data from subjects using electrodes placed on the scalp. Wavelet packet decomposition is used to extract features from the EEG signals, focusing on the alpha frequency band between 8-12 Hz. Learning vector quantization is then used to classify the EEG patterns and identify individuals. The methodology involves preprocessing the EEG data, extracting features using wavelet packet decomposition, and classifying the features with LVQ to identify persons based on their unique EEG signatures.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
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
Prediction Model for Emotion Recognition Using EEGIRJET Journal
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
Mobile Phone Handset Radiation Effect on Brainwave Signal using EEG: A Review IJEEE
This paper talks about the growing concern in the people about the effect of mobile phone RF radiation on human brainwave signal using various techniques to capture those effects. Linear, Non- linear and statistical methods (t-tests) employed by various researchers to analyze variation in brainwaves.This paper gives comparison between brainwaves like Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz) and Gamma (30 Hz-70Hz) in terms of Frequency Progressive map and amplitude progressive tri-maps. Electroencephalography (EEG) gives a noninvasive way of measuring brainwave activity from sensors placed on the scalp of the human head. So, the aim of this paper is to study the effect of RF exposure from mobile phone on Human brainwave as brainwaves Brain wave can provide information of mental state of the individual.
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.
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.
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/
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.
IRJET- Disentangling Brain Activity from EEG Data using Logistic Regression, ...IRJET Journal
This document discusses using machine learning algorithms to analyze EEG data and predict a person's learning capabilities. It extracts features from raw EEG data, including delta, theta, alpha, beta, and gamma waves. It then applies logistic regression, XGBoost, RNN, and decision trees to classify if a student is confused while learning from videos. The highest accuracy was achieved using XGBoost. Overall, the study aims to develop a system to monitor learning using EEG and analyze the correlation between brain activity and learning capability.
IRJET-Estimation of Meditation Effect on Attention Level using EEGIRJET Journal
This document discusses a study that investigates the effect of meditation on attention level using EEG data analysis. EEG data was collected from subjects during meditation and non-meditation states. The data was preprocessed to remove noise and artifacts. Statistical features were then extracted from the EEG data, including standard deviation, relative power, average power spectral density, and entropy. A random forest classification method was used to analyze the data and detect attention states, achieving 90% accuracy. The study aims to objectively measure attention levels and the impact of meditation using EEG analysis to better understand cognitive disorders like ADHD.
POWER SPECTRAL ANALYSIS OF EEG AS A POTENTIAL MARKER IN THE DIAGNOSIS OF SPAS...ijbesjournal
The detection and diagnosis of various neurological disorders are performed using different medical
devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though
significant progress had been made in the analysis of EEG for diagnosis of different neurological
disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the
EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG
patterns could be used for early detection of CP. Twenty children participated in this study out of which ten
were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded
from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15
minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm
in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak
at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of
spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz.
The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can
be used for early detection of spastic CP.
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
Performance Comparison of Known ICA Algorithms to a Wavelet-ICA MergerCSCJournals
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.
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.
This document analyzes spectral features extracted from EEG signals to detect brain tumors. Sixteen candidate features were considered from 102 normal subjects and 100 brain tumor patients. Nine of the features showed a statistically significant difference between the two groups. Specifically, power ratio index, relative intensity ratio for different frequency bands, maximum-to-mean power ratio, peak bispectrum, peak bicoherence, and spectral entropy values were extracted from segmented EEG signals and compared between subjects. Statistical testing found that the mean values of nine features were significantly different between brain tumor patients and normal subjects, suggesting quantitative EEG analysis may help in diagnosis of brain tumors.
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.
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.
EEG S IGNAL Q UANTIFICATION B ASED ON M ODUL L EVELS sipij
This article proposes a contribution to quantify EE
G signals outline. This technique uses two tools fo
r EEG
signal characteristics extraction. Our tests were r
ealized on the basis of 32 canals EEG canals using
Neuroscan software. EEG example demonstration is re
ferenced CZ and is sampled at 1000HZ. The
principal aim of this technique is to reduce the im
portant volume of EEG signal data Without losing an
y
information. EEG signals are quantified on the basi
s of a whole predefined levels The obtained results
show that an EEG alignment can be posted in a quant
ified form.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...
I365358
1. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
A Novelty of Electroencephalogram Signal and Assessment of Sub
Band Data Distribution for Brain Balancing Application.
N. Fuad1, 2, M.N.Taib2, R.Jailani2, M.E.Marwan3
1
(Department of Computer Engineering, Faculty of Electrical and Electronic Engineering, Universiti Tun
Hussein Onn Malaysia, 86400 Johore, MALAYSIA)
2
(Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Selangor, MALAYSIA)
3
(Kolej Poly-Tech MARA Batu Pahat, 83000 Johor, MALAYSIA)
ABSTRACT
The power spectral density (PSD) characteristics extracted from three-dimensional (3D) electroencephalogram
(EEG) models in brain balancing application. There were 51 healthy subjects contributed the EEG dataset.
Development of 3D EEG models involves pre-processing of raw EEG signals and construction of spectrogram
images. The resultant images which are two-dimensional (2D) were constructed via Short Time Fourier
Transform (STFT). Optimization, color conversion, gradient and mesh algorithms are image processing
techniques have been implemented. Then, maximum PSD values were extracted as features. The Shapiro-Wilk
test has been used to check the normality of data distributio and the correlation between sub band has been
analized using Pearson correlation. Results indicate that the proposed maximum PSD from 3D EEG model were
able to distinguish the different levels of brain balancing indexes.
Keywords - power spectral density; 3D EEG model; brain balancing
I. INTRODUCTION
A normal human brain contains a hundred
billions of neurons. About 250,000 neurons are
connected to a single neuron. The information will be
processed by brain and sent signal to whole human
body. An electrical power will be generated and this
signal is named wave [1-4]. Brain is consisted of pair
parts known as left hemisphere and right hemisphere.
The left hemisphere controlled language, arithmetic,
analysis and speech activities. The right side of
hemisphere is dominant in the cognitive tasks such as
understanding, emotion, perceiving, remembering and
thinking [5-8].
The happiness and good health is affected by
healthy lifestyle [9]. The stress feeling and faces
mental illness is caused by disability of mind balance
control. Imbalance lifestyles will be affected by
physical and psychology [11]. The happiness,
satisfaction and healthy condition are achieved when
human mind in balanced condition [10-12]. Previous
studies proved the healthier life can be improving
human potential. Nowadays, the interests to find the
methods for balancing of brain have been increased
[13-15].
The auditory and visual methods in
brainwave entrainment gave positive results in balance
thinking [14-16]. There are other methods namely
Transcranial Magnetic or Electric Stimulation. This
traditional method included massages, meditation and
acupunctures [13-15]. From the review of literature,
most of the human want to feel happy and healthy.
While, a balance life is become from balance thinking
or mind from the brain [1, 17]. Recently, there is no a
scientific proves of brainwave balancing index using
www.ijera.com
EEG. Just some techniques or devices are found to
help human felling clam and brain balancing.
The electroencephalogram (EEG) is a device
to collect brainwave signal named theta-θ, delta-δ ,
alpha-α and beta-β bands are produced [19]. The
EEG raw data is produced in spectral pattern. The
power for each spectral powers has the frequency
bands: theta-θ (4–8 Hz), delta-δ (0.5–4 Hz), alpha-α
(8–13 Hz) and beta-β (13–30 Hz) [20]. These
components are utilized and hypothesized to produce
the variation of neuronal assemblies [1, 21]. In
theoretical, beta band is the lowest amplitude but the
highest frequency band while delta band is opposite to
beta band. High beta is occurred when human is
inactive, not busy or anxious thinking but the low beta
is occurred in positive situations. Human activities
such as closing the eyes, relax/reflecting mode and all
activities with inhibition control are affected by alpha
band. The theta band is occurred when human in stress
mode and light sleep also it has been found in baby
activities. When human is in profound sleep mode, the
delta band is produced [3].
Normally, EEG signals are represented by
time domain and the plot of domain is displayed in
time-amplitude. In the same time, some additional
information can be found from frequency domain
signal. Fourier Transform (FT) is implemented to
produce frequency domain. The artifact in EEG can be
re-referenced in average of EEG power density
spectrum analysis. The result is analyzed using an
algorithm of Fourier Transform (FT) algorithm [22].
Discrete Fourier Transform (FFT) is used to estimate
the smoothed periodograms by the power spectral
53 | P a g e
2. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
density [23]. There are several methods to perform
time-frequency analysis and
Short Time Fourier
Transform (STFT) is one of the method to produced
two dimension (2D) EEG outcome named 2D EEG
image [24]. However, some differences are recognized
among 3D and 2D in term of implementation in
technology field. For examples, parameters for 2D
baby scanning are height and width and 3D baby
scanning are height, width and depth [25]. There are
another research done in 3D implementation such as
crystal surfaces [26], brain-computer interface (BCI)
[27] and assessment some parameters for 3D acoustic
scattering; constant, linear and quadratic [28].
In this research, some methods are proposed
to produce 3D EEG model. The resultant of 3D model
for EEG is shown and the results used to find the
correlation between left and right brainwaves using
features extraction of maximum PSD from 3D model.
The normality is tested using Shapiro-Wilk and
Pearson Correlation in Statistical Package for Social
Science (SPSS).
www.ijera.com
medication before the tests. These are performed and
have fulfilled the requirement provided by ethics
committee from UiTM.
Figure 2 shows the experimental setup for
EEG recording. The EEG data were recorded using 2channels (gold disk bipolar electrode) and a reference
to two earlobes. The electrodes connections comply to
10/20 International system with the sampling rate of
256Hz. Channel 1 positive was connected to the right
hand side (RHS), Fp2. The left hand side (LHS), Fp1
was connected to channel 2 positive. FpZ is the point
at the center of forehead declared as reference point.
MOBIlab was used in wireless EEG equipment and
the EEG signal was monitored for five minutes. The
Z-checker equipment was used to maintain the
impedance to below than 5kΩ. The MATLAB and
SIMULINK are used to process the data with the
intelligent signal processing technique.
II. METHODOLOGY
The flow diagram in figure 1 shows the
methodology of the research. Some processes have
been carried out; data collection, signal pre-processing,
2D and 3D development, features extraction and data
analysis on maximum PSD for evaluation.
Data collection (EEG raw data)
Preprocessing
(Artifact removal and Filtering – delta, theta,
alpha and beta)
Development of 2D images and 3D EEG models
Figure 2. Experimental setup
Feature extraction (PSD)
Data Analysis
(Correlation of Maximum PSD for brain
balancing index)
Figure 1. Flow diagram of methodology
2.1 DATA COLLECTION
This research involved 51 volunteers of
samples with an average age of 21.7. The data are
collected from Biomedical Research and Development
Laboratory for Human Potential, Faculty of Electrical
Engineering, Universiti Teknologi MARA (UiTM)
Malaysia. All volunteers are healthy and not on any
www.ijera.com
2.2 PRE-PROCESSING
The EEG raw data was processed separately
after data collection. The filter of band pass and
artifact removal was included in EEG signal preprocessing. The artifacts may be produced when the
eyes of volunteers blink. The artifacts can be removed
by setting a threshold value in MATLAB tools. The
setting of threshold values were below than -100μV
and greater than 100μV. Only the meaningful and
informatics signal were occurred within -100μV to
100μV. The Hamming windows was used to design
the band pass filter with the rate of overlapping of
50% for the frequency 0.5Hz to 30Hz which were
theta-θ (4–8 Hz), delta-δ (0.5–4 Hz), alpha-α (8–13
Hz) and beta-β (13–30 Hz).
54 | P a g e
3. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
2.3 2D IMAGES USING STFT
The STFT was used to produce the
spectrogram image in 436x342 pixels of image size
for Fp1 and Fp2 channel. Each band of frequency was
set in a spectrogram image. The beta band was set
from 13Hz to 30Hz; delta band was set from 0.5Hz to
4Hz, alpha band (8Hz to 13Hz) and theta band (4Hz to
8Hz). Therefore, the analysis of time frequency
(equation 1) using STFT was performed. The EEG
signal, x(t), the window function, w(t) and signiture of
complex conjugate, * are stated in STFT. The signal
changed in time and performed using STFT. The small
window of data in one time was used to map the signal
to 2D function of time and frequency. Then the
Fourier Transform (FT) would be multiplied with
window function to yield the STFT.
STFT
( w)
x
j 2ft
(t , f ) [ x(t ).(t t ' ).e
dt ]
(1)
2D EEG image named spectrogram is in time
frequency domain.
2.4 3D EEG MODELS
3D EEG models have been developed from
EEG spectrogram using image processing techniques.
Color conversion, gradient, optimization and mesh
algorithms were integrated to developed this model,
while the spectogram images are represented in
RedGreenBlue (RGB) color. Color conversion was
implemented to transform spectogram of RGB to
spectogram of gray scale. Gray scale images were used
in a data matrix (I) which the values represent intensity
within some range which are 0 (black) and 255 (white).
Gray scale is the most commonly used images within
the context of image processing. Equation 2 is
implemented to RGB values of the pixels in the image
to gray scale values of pixels.
(2)
P CR
where C is the column value of the pixel, R is the row
value and P is gray value.
Then, Optimization Options Reference
(OOR) was implemented to gray scale pixels image
for optimization technique. There were severals
options in OOR using MATLAB software but for this
research, DiffMaxChange (Maximum change in
variables for finite differencing) option have been
chosen. The natural shape can be found from pixels
value. This shape related to the maximum of certain
energy function computed from the surface position
and squared norm. A finite number of points were
generated for the height of the optimized surface.
Then the matrices of pixels value were resized using
Gradient and Mesh algorithm into vectors. Two vector
arguments replaced the first two matrix arguments,
length(x) = n and length(y) = m where [m, n] = size
(z). A vectors x is included matrix X (rows) and a
vectors y is for matrix Y (columns). Matrix X and Y
can be evaluated using MATLAB’s array mathematics
features.
www.ijera.com
www.ijera.com
2.5
EEG ANALYSIS
A spectral of PSD was produced from 3D
EEG model, then the maximum PSD was choosed as
features to analyze. Using Shapiro-Wilk technique in
Statistical Package for Social Science (SPSS)
software, the normality is tested. Shapiro-Wilk is
selected because of the small size of samples. If the
value of p is small enough which is less than 0.05 (p <
0.05), the data is considered as significant but not in
normal distribution. Pearson Correlation showed the
correlation between sub band for left and right
brainwaves. The correlation is calculated using the
formula as shown by (equation 3).
( xi x)( yi y)
(3)
Pearson _ Correlation
( N 1) s x s y
where the mean of the sample is represent by
and and xi and yi is the data point and N is the number
of samples. Correlation is the linear relationship
between two variables. Zero correlation indicates that
there is no relationship between the variables.
Correlation of negative 1 indicates a perfect negative
correlation, meaning that as one variable goes up, the
other goes down. Correlation of positive 1 indicates a
perfect positive correlation, meaning that both
variables move in the same direction together.
III. RESULTS AND DISCUSSION
An example of raw EEG signal showed in
figure 3. 2D EEG image named spectrogram is in time
frequency domain. An example of the image is
generated using STFT and the algorithm has explained
previously. The outcome showed in figure 4. The
developments of 3D EEG models have been
successful using optimization; gradient and mesh
algorithms as shown in figure 5 (a)-(h). The 3D model
is spectral of PSD and a different maximum PSD
produced by each frequency band. Eight 3D models
for channels Fp1 and Fp2 are produced by EEG
sample. The 3D model produced as depicted in Table
I.
Figure 3. Raw EEG signal in time domain
55 | P a g e
4. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
www.ijera.com
TABLE III SHAPIRO-WILK TEST OF MAX PSD
FOR EACH SUB BAND
Shapiro-Wilk
Sub band
Statistic
Sig.
0.054
Delta Left
0.956
Delta Right
0.954
0.047
Theta Left
0.966
0.152
Theta Right
0.950
0.030
Alpa Left
0.946
0.022
Alpa Right
0.910
0.001
Beta Left
0.855
0.000
Beta Right
0.884
0.000
Figure 4. 2D EEG image or spectrogram
It shows that p < 0.05 for certain data in bands, so
that the data distributed not in normal pattern (blue
color). In the other hand, the delta right, theta right,
alpha (left and right) side and beta (left and right) side
of the brain fulfill the hypothesis. Some data can be
seen that p > 0.05 and this is true for delta left side and
theta left side. The data is normally distributed (red
color). Therefore the result showed that mixing
between normal distribution and not normal
distribution, resulted to nonparametric types of data.
Figure 6. Data Sample Per Index
3.1
BRAIN BALACING INDEX
The brain balancing index was analyzed
offline from previous work [18]. The percentage
difference between left and right brainwaves was
calculated from PSD values of EEG signals using the
asymmetry formula as shown by (4). Figure 6 shows
the respective index and range of balance score. There
were three groups; index 3 (moderately balanced),
index 4 (balanced) and index 5 (highly balanced).
left right
Percentageof asymmetry 2 x
x100% (4)
3.3 CORRELATION
The confidence interval (significant level, p) for mean
is 95%. Figure 7 depict the Pearson Correlation to
analyze the correlation between sub band for left and
right brainwave. There was a strong positive
relationship between right and left side of brain for all
sub bands with r > 0.5 for all sub bands at left and
right side. For Index 3, alpa band is the highest
correlation values (r=0.960), Index 4 theta band is the
highest (r=0.622) and for Index 5 beta band is the
highest value (r=0.946).
left right
TABLE II BRAIN BALANCING INDEX WITH
RANGE OF BALANCE SCORE
Percentage
Subject
Balanced group/index
difference between
s
left and right
Moderately Balanced 40.0%-59.9%
9
3
Balanced - 4
20.0%-39.9%
37
Highly Balanced - 5
0.0%-19.9%
Figure 7. Pearson Correlation Value of Maximum
PSD for Each Sub Band
5
IV. CONCLUSION
3.2 NORMALITY TEST
Significant level, p which is the confidence
interval for mean is 95%. Table III shows ShapiroWilk test for checking normality of the dependent
variables which is maximum PSD data for each sub
bands left and right.
www.ijera.com
3D EEG model is generated using signal
processing and image processing. The artifact removal
and band pass filter are implemented for preprocessing
signal stage. The resultant images which are twodimensional (2D) EEG image or spectrogram were
constructed via Short Time Fourier Transform (STFT).
Optimization, color conversion, gradient and mesh
56 | P a g e
5. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
algorithms are image processing techniques have been
implemented to produce this model. Results indicate
that the proposed maximum PSD from 3D EEG model
were able to distinguish the different levels of brain
balancing indexes. All bands from the left and right
side of the brain are positively correlated.
[15]
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
Y.M.
Randall
and
C.
O’Reilly,
Computational Exploration in Cognitive
Neuroscience: Understanding the Mind by
Simulating the Brain, MIT Press London,
2000.
D. Cohen, The Secret Language of the Mind,
Duncan Baird Publishers, London, 1996.
M. Teplan, Fundamentals of EEG
Measurement, Measurement Science Review,
vol. 2, pp. 1-11, 2002.
E. R. Kandel, J. H. Schwartz, T. M. Jessell,
Principles of Neural Science, Fourth Edition,
McGraw-Hill, 2000.
E. Hoffmann, Brain Training Against Stress:
Theory, Methods and Results from an
Outcome Study, version 4.2, October 2005.
R. W. Sperry, Left -Brain, Right Brain, in
Saturday Review:speech upon receiving the
twenty-ninth annual Passano Foundation
Award, 1975, pp. 30-33.
R. W. Sperry, Some Effects of Disconnecting
The Cerebral Hemispheres, in Division of
Biology, California Institute of Technology,
Pasadena. California, 1981, pp. 1-9.
Zunairah Haji Murat, Mohd Nasir Taib,
Sahrim Lias, Ros Shilawani S.Abdul Kadir,
Norizam Sulaiman, and Mahfuzah Mustafa.
Establishing the fundamental of brainwave
balancing index (BBI) using EEG, presented
at the 2nd Int. Conf. on Computional
Intelligence, Communication Systems and
Networks (CICSyN2010), Liverpool, United
Kingdom, 2010.
P. J. Sorgi, The 7 Systems of Balance: A
Natural Prescription.
R. W. Sperry, Some Effects of Disconnecting
the Cerebral Hemispheres, in Division of
Biology California Institute of Technology,
Pasadena. California, 1981, pp. 1-9.
P. J. Sorgi, The 7 Systems of Balance: A
Natural Prescription for Healthy Living in a
Hectic World Health Communications
Incorporated, 2002.
E. R. Braverman, The Edge Effect: Archive
Total Health and Longevity: Sterling
Publishing Company, Inc.,2004.
Z. Liu, L. Ding, Integration of EEG/MEG
with MRI and fMRI in Functional
Neuroimaging, IEEE Eng Med Biological
Magazine, vol. 25, pp. 46-53, 2006.
U. Will and E. Berg, Brain Wave
Synchronization and Entrainment to Periodic
www.ijera.com
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
www.ijera.com
Acoustic Stimuli, Neuroscience Letters, vol.
424, pp. 55-60, 2007.
B.-S. Shim, S.-W. Lee, Implementation of a 3
–Dimensional
Game
for
Developing
Balanced Brainwave, presented at 5th
International Conference on Software
Engineering Research, Management &
Applications, 2007.
Rosihan M. Ali and Liew Kee Kor,
Association Between Brain Hemisphericity,
Learning Styles and Confidence in Using
Graphics Calculator for Mathematics,
Eurasia Journal of Mathematics, Science and
Technology Education,vol. 3(2), pp127 -131,
2007.
M. Hutchison, Mega Brain Power:
Transform Your Life with MindMachines and
Brain Nutrients: Hyperion, 1994.
Zunairah Hj. Murat, Mohd Nasir Taib,
Sahrim Lias , Ros Shilawani S. Abdul Kadir,
Norizam Sulaiman and Zodie Mohd
Hanafiah, Development of Brainwave
Balancing Index Using EEG, 2011 Third
International Conference on Computational
Intelligence, Communication Systems and
Networks, pp.374-378, 2011
Jansen BH, Cheng W-K. Structural EEG
analysis: an explorative study, Int J Biomed
Comput 1988; 23: 221-37.
L. Sornmo, and P. Laguna, Bioelectrical
Signal Processing in Cardiac and
Neurological Applications. Burlington, MA:
Elsevier Academic Press, 2005.
N. Hosaka, J. Tanaka, A. Koyama, K.
Magatani, The EEG measurement technique
under exercising, Proceedings of the 28th
IEEE
EMBS
Annual
International
Conference, New York City, USA, Sept
2006, pp. 1307-1310.
A. Delorme, and S. Makeig, “The
EEGLAB,” Internet http://www. sccn.ucsd.
edu/eeglab, vol. 2, no. 004, pp. 1.2.
C. Babiloni, G. Binetti, E. Cassetta, D.
Cerboneschi, G. D. Forno, C. D.Percio, F.
Ferreri, R. Ferri, B. Lanuzza, C. Miniussi, D.
V. Moretti, F. Nobili, R. D. Pascual-Marqui,
G. Rodriguez, G. L. Romani, S. Salinari, F.
Tecchio, P. Vitali,O. Zanetti, F. Zappasodi,
P. M. Rossin., Mapping distributed sources
of cortical rhythms in mild Alzheirmer's
disease.
A
multicentric
EEGstudy,
NeuroImage, vol. 22, pp. 57-67, 2004.
K. N. Diaye, R. Ragot, L. Garnero, V.
Pouthas , What is common to brain activity
evoked by the perception of visual and
auditory filled durations? A study with MEG
and EEG co-recordings, Cognitive Brain
Research,vol. 21, pp. pp. 250-268, 2004.
C. Babiloni, R. Ferri, G. Binetti, F. Vecchio,
G. B. Frisoni, B. Lanuzza, C. Miniussi, F.
57 | P a g e
6. N. Fuad et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 6, Nov-Dec 2013, pp.53-58
Nobili, G. Rodriguez, F. Rundo, A.
Cassarino, F. Infarinato, E. Cassetta, S.
Salinari, F. Eusebi, and P. M. Rossini,
Directionality of EEG synchronization in
Alzheimer's disease subjects, Neurobiology
of Aging, vol. 30, pp. 93-102, 2009.
[26] A. Piryatinska, G. Terdik, W. A.
Woyczynski, K. A. Loparo, M. S. Scher, and
A. Zlotnik, Automated detection of neonate
EEG sleep stages,Computer Methods and
Programs in Biomedicine, In Press,
Corrected Proof.
Appendix A
(a)
[27]
[28]
www.ijera.com
M. T. Pourazad, Z. K. Mousavi, and G.
Thomas, Heart sound cancellation from lung
sound recordings using adaptive threshold
and 2D interpolation in time-frequency
domain, in Proceedings of the 25th Annual
International Conference of the IEEE, 2003,
pp. 2586-2589.
Ohbuchi.R, Incremental 3D ultrasound
imaging from a 2D scanner , Conference in
Biomedical Computing, Atlanta, 1990.
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 5. Three Dimension (3D) model for (a) Delta band from Fp1 channel (b) Delta band from Fp2 channel
(c) Theta band from Fp1 channel (d) Theta band from Fp2 channel (e) Alpha band from Fp1 channel (f) Alpha
band from Fp2 channel (g) Beta band from Fp1 channel (h) Beta band from Fp2 channel
www.ijera.com
58 | P a g e