New ways in non-stationary , nonlinear EEG 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]

OBJECTIVE Those who analyze EEG data require quantitative techniques that can be validly applied to time series exhibiting ranges of non stationary behavior. ...

Journal of Medical Systems, 2010

The EEG (Electroencephalogram) signal indicates the electrical activity of the brain. They are highly random in nature and may contain useful information about the brain state. However, it is very difficult to get useful information from these signals directly in the time domain just by observing them. They are basically non-linear and nonstationary in nature. Hence, important features can be

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

Journal of Neuroscience Methods, 2005

Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time-frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time-frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time-frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time-frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time-frequency domain and for studying the intricate, frequency-based dynamics of the human brain.

The record of human brain neural activities, namely electroencephalogram (EEG), is known to be nonstationary in general. In addition, the human head is a non-linear medium for such signals. In many applications, it is useful to divide the EEGs into segments in which the signals can be considered stationary. Here, Hilbert-Huang Transform (HHT), as an effective tool in signal processing is applied since unlike the traditional time-frequency approaches, it exploits the non-linearity of the medium and nonstationarity of the EEG signals. In addition, we use Singular Spectrum Analysis (SSA) in the pre-processing step as an effective noise removal approach. By using synthetic and real EEG signals, the proposed method is compared with Wavelet Generalized Likelihood Ratio (WGLR) algorithm as a well-known signal segmentation method. The simulation results indicate the performance superiority of the proposed method.

Pomiary Automatyka Robotyka

Neurophysiologie Clinique-clinical Neurophysiology - NEUROPHYSIOL CLIN, 2002

IEEE Transactions on Biomedical Engineering, 2007

Most neurological signals including electroencephalogram (EEG), evoked potential (EP) and local field potential (LFP) have been known to be time varying and nonstationary, especially in some pathological conditions. Currently, the most widely used quantitative tool for such nonstationary signals is time-frequency representation (TFR) which demonstrates the temporal evolution of different frequency components. However, TFR does not directly provide a quantitative measure of nonstationarity level, e.g., how far the process deviates from stationarity. In this study, we introduced three different quantifications of TFR (qTFR) to characterize the nonstationarity level of the involving signals: 1) degree of stationarity (DS); 2) Shannon entropy (SE) of the marginal spectrum; and 3) Kullback-Leibler distance (KLD) between a TFR and a uniform distribution. These descriptors provide quantitative analysis of stationarity of a signal such that the stationarity of different signals could be compared. In this study, we obtained the TFRs of the EEG signals before and after the hypoxic-ischemic (HI) brain injury and examined the stationarity of the EEG. DS, SE, and KLD can indicate the nonstationarity change of EEG at each frequency following the HI injury, especially in the upper-and lower-band (e.g., [2 Hz 8 Hz]) as well as in the 2 band (e.g., [22 Hz-26 Hz]). Moreover, it is shown that the stationarity of the EEG changes differently in different frequencies following the HI injury.

2011

Abstract This paper presents an introduction to time-frequency (TF) methods in signal processing, and a novel approach for EEG abnormalities detection and classification based on a combination of signal related features and image related features. These features which characterize the non-stationary nature and the multi-component characteristic of EEG signals, are extracted from the TF representation of the signals.