scholarly journals Removal of eye-blink artifacts from EEG signal

2020 ◽  
Vol 40 (2) ◽  
pp. 101-111
Author(s):  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Brain Activity ◽  
Independent Component ◽  
Signal Decomposition ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Eye Blink ◽  
Electrical Brain Activity ◽  
Spatial Features ◽  
Challenging Tasks ◽  
Ica Algorithm

Electroencephalography (EEG) is well known method of recording electrical brain activity with electrodes placed along the scalp. One of the challenging tasks in this field is the removal of electrical signals that are not related to brain activity.In this paper, an algorithm for the removal of the EEG signals corresponding to the eye blink artifacts is presented. The presented algorithm is based on ADJUST artifact removing tool, which uses independent component analysis (ICA) for signal decomposition. For every signal component returned by the ICA algorithm, temporal-spatial features are calculated, upon which every independent component is classified as artifact or non-artifact, and removed accordingly.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

scholarly journals Increased signal diversity/complexity of spontaneous EEG, but not evoked EEG responses, in ketamine-induced psychedelic state in humans

2019 ◽  
Author(s):  
Nadine Farnes ◽  
Bjørn E. Juel ◽  
André S. Nilsen ◽  
Luis G. Romundstad ◽  
Johan F. Storm
Keyword(s):  
Brain Activity ◽  
Altered States Of Consciousness ◽  
Altered States ◽  
Eeg Signal ◽  
Eyes Closed ◽  
Electrical Brain Activity ◽  
States Of Consciousness ◽  
Significant Difference ◽  
Eyes Open ◽  
Signal Diversity

AbstractObjectiveHow and to what extent electrical brain activity is affected in pharmacologically altered states of consciousness, where it is mainly the phenomenological content rather than the level of consciousness that is altered, is not well understood. An example is the moderately psychedelic state caused by low doses of ketamine. Therefore, we investigated whether and how measures of evoked and spontaneous electroencephalographic (EEG) signal diversity are altered by sub-anaesthetic levels of ketamine compared to normal wakefulness, and how these measures relate to subjective assessments of consciousness.MethodsHigh-density electroencephalography (EEG, 62 channels) was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and after administration of sub-anaesthetic doses of ketamine in an open-label within-subject design. Evoked signal diversity was assessed using the perturbational complexity index (PCI), calculated from the global EEG responses to local TMS perturbations. Signal diversity of spontaneous EEG, with eyes open and eyes closed, was assessed by Lempel Ziv complexity (LZc), amplitude coalition entropy (ACE), and synchrony coalition entropy (SCE).ResultsAlthough no significant difference was found in the index of TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all the three measures of spontaneous EEG signal diversity showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity also correlated with subjective assessment of altered states of consciousness. Moreover, spontaneous signal diversity was significantly higher when participants had eyes open compared to eyes closed, both during normal wakefulness and during influence of sub-anaesthetic ketamine doses.ConclusionThe results suggest that PCI and spontaneous signal diversity may be complementary and potentially measure different aspects of consciousness. Thus, our results seem compatible with PCI being indicative of the brain’s ability to sustain consciousness, as indicated by previous research, while it is possible that spontaneous EEG signal diversity may be indicative of the complexity of conscious content. The observed sensitivity of the latter measures to visual input seems to support such an interpretation. Thus, sub-anaesthetic ketamine may increase the complexity of both the conscious content (experience) and the brain activity underlying it, while the level, degree, or general capacity of consciousness remains largely unaffected.

scholarly journals Epileptic Detection from the Eeg Signal using the Anti colony Optimization Technique with Deep Neural Network

2020 ◽  
Vol 9 (1) ◽  
pp. 2726-2733
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Optimization Technique ◽  
Signal Decomposition ◽  
Ant Colony ◽  
Series Data ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mode Decomposition

Extensively used technique to diagnose the epilepsy is EEG. The research objective is to check the variations of frequency found in the epileptic EEG signals.. The EEG dataset were acquired from online database of the Bonn University (BU). Then, butterworth type two filter was implemented to remove the unwanted artifacts from the acquired EEG signals. Further, Multivariate Variational Mode Decomposition (MVMD) methodology was applied to decompose the denoised EEG signals. The signal decomposition helps in finding the necessary information, which required to model the complex time series data. Then, the features were extracted from decomposed signals by using fifteen entropy, linear and statistical features. In addition, ant colony optimization technique was proposed for optimizing the extracted features. The optimized feature vectors were classified by Deep Neural Network (DNN) that includes two circumstances (seizure and healthy), and (Interictal, ictal, and normal). The accuracy attained using the ant colony with deep neural network is 98.12% using the BU EEG dataset, respectively related to the existing models.

scholarly journals Classification of Alcoholic and Non-Alcoholic EEG Signals Based on Sliding-SSA and Independent Component Analysis

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Machine Learning ◽  
Independent Component Analysis ◽  
Singular Spectrum Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Computational Time ◽  
Learning Models ◽  
Eeg Signals ◽  
Ica Algorithm ◽  
Machine Learning Models

<pre>Alcoholism is a widely affected disorder that leads to critical brain deficiencies such as emotional and behavioural impairments. One of the prominent sources to detect alcoholism is by analysing Electroencephalogram (EEG) signals. Previously, most of the works have focused on detecting alcoholism using various machine and deep learning algorithms. This paper has used a novel algorithm named Sliding Singular Spectrum Analysis (S-SSA) to decompose and de-noise the EEG signals. We have considered independent component analysis (ICA) to select the prominent alcoholic and non-alcoholic components from the preprocessed EEG data. Later, these components were used to train and test various machine learning models like SVM, KNN, ANN, GBoost, AdaBoost and XGBoost to classify alcoholic and non-alcoholic EEG signals. The sliding SSA-ICA algorithm helps in reducing the computational time and complexity of the machine learning models. To validate the performance of the ICA algorithm, we have compared the computational time and accuracy of ICA with its counterpart, like principal component analysis (PCA). The proposed algorithm is tested on a publicly available UCI alcoholic EEG dataset. To verify the performance of machine learning models, we have calculated various metrics like accuracy, precision, recall and F1 score. Our work reported the highest accuracy of 98.97% with the XGBoost classifier. The validation of the proposed method is done by comparing the classification metrics with the latest state-of-the-art works.</pre>

scholarly journals Emotion recognition using electroencephalogram signal

2019 ◽  
Vol 15 (2) ◽  
pp. 786
Author(s):  
Aqila Nur Nadira Mohammad Yosi ◽  
Khairul Azami Sidek ◽  
Hamwira Sakti Yaacob ◽  
Marini Othman ◽  
Ahmad Zamani Jusoh
Keyword(s):  
High Resolution ◽  
Emotion Recognition ◽  
Brain Activity ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Recognition Mechanism ◽  
High Resolution Data ◽  
Average Percentage ◽  
Low Pass ◽  
Acquisition Device

<p class="Abstract">Emotion play an essential role in human’s life and it is not consciously controlled. Some of the emotion can be easily expressed by facial expressions, speech, behavior and gesture but some are not. This study investigates the emotion recognition using electroencephalogram (EEG) signal. Undoubtedly, EEG signals can detect human brain activity accurately with high resolution data acquisition device as compared to other biological signals. Changes in the human brain’s electrical activity occur very quickly, thus a high resolution device is required to determine the emotion precisely. In this study, we will prove the strength and reliability of EEG signals as an emotion recognition mechanism for four different emotions which are happy, sad, fear and calm. Data of six different subjects were collected by using BrainMarker EXG device which consist of 19 channels. The pre-processing stage was performed using second order of low pass Butterworth filter to remove the unwanted signals. Then, two ranges of frequency bands were extracted from the signals which are alpha and beta. Finally, these samples will be classified using MLP Neural Network. Classification accuracy up to 91% is achieved and the average percentage of accuracy for calm, fear, happy and sad are 83.5%, 87.3%, 85.83% and 87.6% respectively. Thus, a proof of concept, this study has been capable of proposing a system of recognizing four states of emotion which are happy, sad, fear and calm by using EEG signal.</p>

scholarly journals EMBEDDED IMPLEMENTATION OF EEG ANALYSIS USING INDEPENDENT COMPONENT APPROACH

2012 ◽  
pp. 11-16
Author(s):  
FASEELA.K. P ◽  
SUPRIYA. P
Keyword(s):  
Brain Regions ◽  
Independent Component ◽  
Eeg Analysis ◽  
Eeg Signals ◽  
Brain Signals ◽  
Component Approach ◽  
Embedded Development ◽  
Ica Algorithm ◽  
Embedded Implementation ◽  
Original Information

Brain signals are important in diagnosing various disorders and abnormalities in the human body. These signals are recorded by scalp electrodes and are called as EEG signals. EEG signals are a mixture of signals from different brain regions which contain artefacts along with original information. These contaminated mixtures are analysed such that diagnosis of various diseases is possible. One of the effective methods available is Independent Component Analysis (ICA) for removing artefacts and for separation and analysis of the desired sources from within the EEGs. This paper focuses on the analysis of EEG signals using ICA approach. Two ICA algorithms- Pearson ICA and JADE ICA are analysed in this paper. Comparison of these ICA algorithms in removing artefacts from EEG has been carried out by simulation using MATLAB. Then the Pearson ICA algorithm simulation is done using Visual C#. The algorithm has been implemented in an Embedded Development Kit (EDK) using .NET Micro Framework and the results are presented.

scholarly journals Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

2019 ◽  
Vol 15 (1) ◽  
pp. 1-12
Author(s):  
Ali Al-Saegh
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Speaker Identification ◽  
Component Analysis ◽  
Independent Component ◽  
Eeg Signals ◽  
Artifact Detection ◽  
Eeg Data ◽  
Ica Algorithm ◽  
Complex Valued

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

scholarly journals Best Feature Extraction and Classification Algorithms for EEG Signals in Neuromarketing

2020 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Boniadi Naieni
Keyword(s):  
Feature Extraction ◽  
Brain Activity ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
High Efficient ◽  
The Brain

Purpose: There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for revealing Consumer behavior by extracting brain activity. Functional Magnetic Resonance Imaging (fMRI), Magnetoencephalography (MEG), and Electroencephalography (EEG) are high efficient tools for investigating the brain activity in neuromarketing. EEG signal is a high temporal resolution and a cheap method for examining the brain activity. Materials and Methods: 32 subjects (16 males and 16 females) aging between 20-35 years old participated in this study. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods are applied on the EEG data such as Discrete Wavelet Transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods were used for whole and lobe brain data. Results: The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters were used for evaluation of the model performance. The results show high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data. Conclusion: We designed a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigated the effects of advertising on brain activity of consumers by EEG signals measures.

Information-Based Classification of Electroencephalography (EEG) Signals for Healthy Adolescents and Adolescents with Symptoms of Schizophrenia

2020 ◽  
Vol 19 (04) ◽  
pp. 2050033 ◽  
Author(s):  
Hamidreza Namazi
Keyword(s):  
Information Content ◽  
Shannon Entropy ◽  
Healthy Subjects ◽  
Brain Activity ◽  
Research Area ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
First Time ◽  
The Brain

Analysis of the brain activity is the major research area in human neuroscience. Besides many works that have been conducted on analysis of brain activity in case of healthy subjects, investigation of brain activity in case of patients with different brain disorders also has aroused the attention of many researchers. An interesting category of works belong to the comparison of brain activity between healthy subjects and patients with brain disorders. In this research, for the first time, we compare the brain activity between adolescents with symptoms of schizophrenia and healthy subjects, by information-based analysis of their Electroencephalography (EEG) signals. For this purpose, we benefit from the Shannon entropy as the indicator of information content. Based on the results of analysis, EEG signal in case of healthy subjects contains more information than EEG signal in case of subjects with schizophrenia. The result of statistical analysis showed the significant variation in the Shannon entropy of EEG signal between healthy adolescents and adolescents with symptoms of schizophrenia in case of P3, O1 and O2 channels. The employed method of analysis in this research can be further extended in order to investigate the variations in the information content of EEG signal in case of subjects with other brain disorders versus healthy subjects.

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