scholarly journals Removal of Artifacts from EEG Signals: A Review

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 987 ◽  
Author(s):  
Xiao Jiang ◽  
Gui-Bin Bian ◽  
Zean Tian
Keyword(s):  
Comparative Analysis ◽  
Open Problem ◽  
State Of The Art ◽  
Brain Activity ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Data ◽  
And Behavior ◽  
Electroencephalogram Eeg

Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.

scholarly journals Computational Models in Electroencephalography

2021 ◽  
Author(s):  
Katharina Glomb ◽  
Joana Cabral ◽  
Anna Cattani ◽  
Alberto Mazzoni ◽  
Ashish Raj ◽  
...  
Keyword(s):  
In Silico ◽  
Computational Models ◽  
State Of The Art ◽  
Brain Activity ◽  
Eeg Signal ◽  
Comprehensive Understanding ◽  
Healthcare Applications ◽  
The One ◽  
And Behavior ◽  
Design Experiments

AbstractComputational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

scholarly journals Denoising of EEG signal based on word imagination using ICA for artifact and noise removal on unspoken speech

2021 ◽  
Vol 22 (1) ◽  
pp. 83
Author(s):  
Efy Yosrita ◽  
Rosida Nur Aziza ◽  
Rahma Farah Ningrum ◽  
Givary Muhammad
Keyword(s):  
Noise Removal ◽  
Physical Activities ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Signal Ratio ◽  
Eye Blink ◽  
Eeg Data ◽  
Word Classification ◽  
Electroencephalogram Eeg ◽  
Electrical Activities

<span>The purpose of this research is to observe the effectiveness of independent component analysis (ICA) method for denoising raw EEG signals based on word imagination, which will be used for word classification on unspoken speech. The electroencephalogram (EEG) signals are signals that represent the electrical activities of the human brain when someone is doing activities, such as sleeping, thinking or other physical activities. EEG data based on the word imagination used for the research is accompanied by artifacts, that come from muscle movements, heartbeat, eye blink, voltage and so on. In previous studies, the ICA method has been widely used and effective for relieving physiological artifacts. Artifact to signal ratio (ASR) is used to measure the effectiveness of ICA in this study. If the ratio is getting larger, the ICA method is considered effective for clearing noise and artifacts from the EEG data. Based on the experiment, the obtained ASR values from 11 subjects on 14 electrodes amounted are within the range of 0,910 to 1,080. Thus, it can be concluded that ICA is effective for removing artifacts from EEG signals based on word imagination.</span>

scholarly journals Maximum Marginal Approach on EEG Signal Preprocessing for Emotion Detection

2020 ◽  
Vol 10 (21) ◽  
pp. 7677
Author(s):  
Gen Li ◽  
Jason J. Jung
Keyword(s):  
Feature Selection ◽  
Eeg Signal ◽  
Emotion Detection ◽  
Eeg Signals ◽  
Training Time ◽  
Eeg Data ◽  
Signal Preprocessing ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Signal Similarity

Emotion detection is an important research issue in electroencephalogram (EEG). Signal preprocessing and feature selection are parts of feature engineering, which determines the performance of emotion detection and reduces the training time of the deep learning models. To select the efficient features for emotion detection, we propose a maximum marginal approach on EEG signal preprocessing. The approach selects the least similar segments between two EEG signals as features that can represent the difference between EEG signals caused by emotions. The method defines a signal similarity described as the distance between two EEG signals to find the features. The frequency domain of EEG is calculated by using a wavelet transform that exploits a wavelet to calculate EEG components in a different frequency. We have conducted experiments by using the selected feature from real EEG data recorded from 10 college students. The experimental results show that the proposed approach performs better than other feature selection methods by 17.9% on average in terms of accuracy. The maximum marginal approach-based models achieve better performance than the models without feature selection by 21% on average in terms of accuracy.

scholarly journals Kombinasi Sinyal EEG dan Giroskop untuk Kendali Mobil Virtual dengan Menggunakan Modifikasi ICA dan SVM

2016 ◽  
Vol 7 (3) ◽  
Author(s):  
Ahmad Reza Musthafa ◽  
Handayani Tjandrasa
Keyword(s):  
Support Vector Machine ◽  
Brain Computer Interface ◽  
Signal Classification ◽  
Computer Interface ◽  
Support Vector ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Artifact Removal ◽  
Eeg Signal Classification ◽  
Electroencephalogram Eeg

Abstract. Electroencephalogram (EEG) signals has been widely researched and developed in many fields of science. EEG signals could be classified into useful information for the application of Brain Computer Interface topic (BCI). In this research, we focus in a topic about driving a car using EEG signal. There are many approaches in EEG signal classification, but some approaches do not robust EEG signals that have many artifacts and have been recorded in real time. This research aims to classify EEG signals to obtain more optimal results, especially EEG signals with many artifacts and can be recorded in realtime. This research uses Emotiv EPOC device to record EEG signals in realtime. In this research, we propose the combination of Automatic Artifact Removal (AAR) and Support Vector Machine (SVM) which has 71% of accuracy that can be applied to drive a virtual car.Keyword: EEG signal classification, automatic artifact removal, brain computer interface Abstrak. Penelitian berbasis sinyal Electroencephalogram (EEG) telah banyak diteliti dan dikembangkan pada berbagai bidang ilmu pengetahuan. Sinyal EEG dapat diklasifikasikan ke dalam bentuk informasi untuk pengaplikasian topik Brain Computer Interface (BCI). Pada penelitian ini difokuskan pada topik pengendalian mobil menggunakan perintah sinyal EEG. Terdapat beberapa pendekatan dalam klasifikasi sinyal EEG, tetapi beberapa pendekatan tersebut tidak robust terhadap sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini bertujuan untuk mengklasifikasikan sinyal EEG dengan hasil lebih optimal, khususnya pada sinyal EEG yang memiliki banyak artefak dan direkam secara realtime. Penelitian ini menggunakan perangkat Emotiv EPOC untuk merekam sinyal EEG secara realtime. Pada penelitian ini diusulkan kombinasi Automatic Artifact Removal (AAR) dan Support Vector Machine (SVM) yang menghasilkan hasil akurasi sebesar 71% untuk klasifikasi sinyal EEG pada kasus pengendalian mobil virtual.Kata Kunci: EEG signal classification, automatic artifact removal, brain computer interface

scholarly journals Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music

2020 ◽  
Vol 32 (4) ◽  
pp. 724-730
Author(s):  
Shin-ichi Ito ◽  
◽  
Momoyo Ito ◽  
Minoru Fukumi
Keyword(s):  
Brain Activity ◽  
Temporal Cortex ◽  
Human Cognition ◽  
Support Vector ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
Eeg Data ◽  
Vector Machines ◽  
Electroencephalogram Eeg ◽  
Activity Sensing

We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into “wants,” “not wants,” and “other feelings.” In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58%, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVM are effective for the detection of human wants.

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.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals EEG data processing in ADHD diagnosis and neurofeedback

2020 ◽  
Vol 40 (3) ◽  
pp. 116-123
Author(s):  
Zoran Šverko ◽  
Ivan Markovinović ◽  
Miroslav Vrankić ◽  
Saša Vlahinić
Keyword(s):  
Data Processing ◽  
Mean Value ◽  
Adhd Group ◽  
Eeg Signal ◽  
Statistical Features ◽  
Eeg Signals ◽  
New Approach ◽  
Mean Values ◽  
Adhd Diagnosis ◽  
Eeg Data

In this paper, EEG data processing was conducted in order to define the parameters for neurofeedback. A new survey was conducted based on a brief review of previous research. Two groups of participants were chosen: ADHD (3) and nonADHD (14). The main part of this study includes EEG signal data pre-processing and processing. We have outlined statistical features of observed EEG signals such as mean value, grand-mean value and their ratios. It can be concluded that an increase in grand-mean values of power theta-low beta ratio on Cz electrode gives confirmation of previous research. The value of alpha-delta power ratio higher than 1 on C3, Cz, P3, Pz, P4 in ADHD group is proposed as a new approach to classification. Based on these conclusions we will design a neurofeedback protocol as a continuation of this work.

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