EEG Signal Analysis: A Survey

2014

The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human-robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer's disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

The Electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about the state of the brain. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. Chaotic measures like correlation dimension (CD), largest Lyapunov exponent (LLE), Hurst exponent (H) and entropy are used to characterize the signal. Results indicate that these nonlinear measures are good discriminators of normal and epileptic EEG signals. These measures distinguish epileptic EEG and alcoholic from normal EEG with an accuracy of more than 90%. The dynamical behavior is less random for alcoholic and epileptic compared to normal. This indicates less of information processing in the brain due to the hyper-synchronization of the EEG. Hence, the application of nonlinear time series analysis to EEG signals offers insight into the dynamical nature and variability of the brain signals.

International Journal of Engineering Research and, 2020

This paper deals with the basics about electroencephalogram, its processing and feature extractions. Prominently used extraction methods such as Principal Component Analysis, Independent Component Analysis, Time-Frequency Analysis, Wavelet Transform have been discussed here along with mathematical representations. Software tools and their use towards EEG are highlighted.

2019

Electroencephalography (EEG) is one of the most important signals recorded from humans. It can assist scientists and experts to understand the most complex part of the human body, the brain. Thus, analysing EEG signals is the most preponderant process to the problem of extracting significant information from brain dynamics. It plays a prominent role in brain studies. The EEG data are very important for diagnosing a variety of brain disorders, such as epilepsy, sleep problems, and also assisting disability patients to interact with their environment through brain computer interface (BCI). However, the EEG signals contain a huge amount of information about the brain's activities. But the analysis and classification of these kinds of signals is still restricted. In addition, the manual examination of these signals for diagnosing related diseases is time consuming and sometimes does not work accurately. Several studies This Thesis is the work of Hadi Ratham Ghayab Al Ghayab except where otherwise acknowledged, with the majority of the authorship of the papers presented as a Thesis by Publication undertaken by the Student. The work is original and has not previously been submitted for any other award, except where acknowledged.

Un ni iv ve er rs si it tá á d di i B Br re es sc ci ia a, , I It ta aly Abstract: The EEG is composed of electrical potentials arising from several sources. Each source (including separate neural clusters, blink artifact or pulse artifact) forms a unique topography onto the scalp -'scalp map'. Scalp map may be 2-D or 3-D.These maps are mixed according to the principle of linear superposition. Independent component analysis (ICA) attempts to reverse the superposition by separating the EEG into mutually independent scalp maps, or components. MATLAB toolbox and graphic user interface, EEGLAB is used for processing EEG data of any number of channels. Wavelet toolbox has been used for 2-D signal analysis.

IRJET, 2023

Electroencephalogram (EEG) signal is the most effective, quick, and abundant source of information in understanding the brain related phenomenon. New avenues for EEG-based research in non-medical streams can also be seen with the growing number of qualitative and affordable wearable EEG headsets. But it is extremely hard to assess the information from EEG signal. However, information-theoretical approaches have appeared as a potentially beneficial means to gauge variations in the EEG datasets. This article discusses one such approach: the 'measure of Tsallis entropy (TsEn)' to explore and investigate the available natural data. This study set out to critically review the renowned research papers on Tsallis entropy-based EEG signal processing to understand the trends in EEG signal processing research. It attempts to provide practitioners and researchers with insights and future directions for applicability of Tsallis entropy for EEG signal processing and with an emphasis on the suitability of EEG research for clinical studies. It reviews about 35 published papers dividing into medical and non-medical domains and discusses the crucial role of Tsallis parameter 'q' in studying complex EEG systems. The result shows Tsallis's non-extensive initiatives seem to be more discriminatory than its Shannon counterpart and all other entropy variants and hence, can preferably be used to study the brain. The paper also concludes that Tsallis entropy offers a comprehensive test of any theory and it proves the efficacy of EEG research in clinical detection and therefore is highly significant in biomedical signal processing.

Tikrit Journal of Pure Science (TJPS) , 2005

This research deals on a study that illustrate the use of time-frequency analysis method for evaluating latent structure in nonstationary electroencephalographic (EEG) traces obtained from one healthy young person and in (9) types of experiments. The data collected for the analysis with sampling frequency up to 1KHz. We explore and illustrate the use of time-frequency analysis method. Analysis of the patterns of change over time in the frequency structure of such EEG data provides insight into the neurophsiological mechanism of action of this effective. Higher-order spectral analysis (Hosa) is applied to analysis of EEG in order to investigate the time and motion of EEG signals. The wigner spectrum is proposed in this paper for the purpose of extracting more information beyond higher-order spectral. The actual EEG with normal subject in several different functional states of brain are analyzed in terms of the parametric spectral estimation. The experimental results show that all kinds of spontaneous EEG exhibit obvious interactions of EEG signals, but the wigner spectral pattern of normal EEG changes with different functional states of brain. It is suggest that the Wigner spectrum could be regarded as the main feature in the study of EEG signals and provide effective quantitve measure for analyzing and processing electroencephalography in different physiological states of brain. 2 Introduction EEG signals are the variations of potential in the cortex or on the surface of scalp causing by physiological activities of the brain. Detecting the changes of these waves is critical for the understanding of brain functions [2]. Although we know much of the structure and functional attributes of the brain, and can understand some of the its mechanisms of information processing, we lake certainty regarding the overall dynamical properties of the brain, that is, we have little idea how the subcomponents of the brain are integrated into a functional whole. Recognizing that the problem of the brain's overall integration raises poorly posed but important questions. [3] A large amount of information is present in the EEG signals, much of which cannot be adequately appreciated by visual inspection of analog tracings of the activity. Included in this activity are such measures as the precise value of the dominant frequency and the correspondence between signals recorded from matching points on the two side of the scalp. [4] EEG traces are time series of electrical potential fluctuations recorded at various scalp locations that reflect the physiological behavior of the underlying brain cells (weiner, Krystal)[6]

2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), 2014

In this paper comparision of two, novel signal processing methods for analysis of EEG and EMG biomedical signals was presented. This is because nowadays analysis of biosignals is very popular. The first method described in this paper applies kernel density estimators, which enable densitograms construction of the examined biomedical signals. The advantage of this method is that it allowes to obtain statistically filtered signals and makes the whole process quicker. The second method presented in this paper is based on basic mathematical operations only, which also simplifies the whole process of analysis. the second method is also quick and efficient, with wide potential spectrum of use as it can also be implemented on embedded platform and the algorithm can be reqritten in any programming language.

— EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. MATLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG dataset and other brain signal data different techniques such as independent component analysis (ICA) and/or time/frequency analysis (TFA), as well as standard averaging methods. We will be showing different brain signals by comparing, analysing and simulating datasets which is already loaded in the MATLAB software to process the EEG signals. Keyword-EEG, Signal processing, MATLAB, Brainwaves, Diagnosis I. INTRODUCTION The human brain is one of the most complex systems in the universe. Nowadays various technologies exist to record brain waves and electroencephalography (EEG) is one of them. This is one of the brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. II. BRAIN SIGNAL PROCESSING Signal processing is the enabling technology for the generation, transformation, and interpretation of information. At different stages of time our brain reacts differently. These brain signals used for various purposes so that it is possible to study the functionalities of brain properly by generating, transforming and interpreting the collected signal. This process is known as brain signal processing. A. Brain Waves and EEG The analysis of brain waves plays an important role in diagnosis of different brain disorders. Brain is made up of billions of brain cells called neurons, which use electricity to communicate with each other. The combination of millions of neurons sending signals at once produces an enormous amount of electrical activity in the brain, which can be detected using sensitive medical equipment such as an EEG which measures electrical levels over areas of the scalp. The electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. [5] The combination of electrical activity of the brain is commonly called a Brainwave pattern because of its wave-like nature. III. OVERVIEW EEG signals contain more relevant information about brain disorders and different types of artifacts. Signals in the form of dataset are already loaded to the tool so we will be using that signals to plot the data and visualization of the time-frequency domain plots which can be displayed all together. Basically we will be monitoring the EEG signals according to the placement of electrodes which is called montages. After that we will observe the EEG signals to recognize and eliminate different disease related artifacts. Then unwanted signal will be subtracted by differential amplifier. Finally we will proceed for the signal filtering based on the different types of brainwave frequencies to diagnosis and simulate variety of brain disorders by using MATLAB.