EEG Signal of Epiliptic Patient by Fast Fourier and Wavelet Transforms

Mental state detection is the need of today's age due to increase in cases of mental disorders. Emotion describes the current mental state of the human being. The development of Emotion based Non Invasive Electroencephalogram brain-computer interface will be useful to analyze brain activity and to read hidden brains of people in need that most of us take for granted. The behavior of Electroencephalogram EEG signal is categorized in linear, nonlinear, stationary and non stationary. Behavioral analysis of the EEG signal is necessary to understand complex brain activity. The focus of this paper is the Initial analysis of brain EEG signal for mental state detection of human being. This paper presents initial analysis of EEG signal, databases and emotion classification system for the development of Intelligent Emotion Recognition System.

본 논문에서는 의료 서비스를 위한 뇌전기파(EEG: electroencephalogram) 신호 분석용 FFT(Fast Fourier Transform) 프로세서를 구현하였다. 실시간으로 발생하는 EEG 신호를 블록으로 나누어 short-time FFT 처리하기 위해 Hamming 창 함수를 사용하였으며, 이로 인해 감소되는 양끝의 값은 1/2 오버랩 시켜 보완하였다. 0~100 [Hz] 사이의 주파수 특성을 갖는 뇌전기파의 효율적인 대역 분석을 위해 256-point FFF 프로세서를 radix-4 알고리듬을 적용하여 구현하였으며, 단일 메모리 뱅크 구조를 사용하여 집적도를 높였다. 설계된 FFT 프로세서는 FPGA 구현을 통해 가능을 검증하였으며, 연산오차가 2% 이내로 높은 연산 정밀도를 갖는다. This paper describes a design of fast Fourier transform(FFT) processor for EEG(electroencephalogram) signal analysis for health care services. Hamming window function with 1/2 overlapping is adopted to perform short-time FFT(ST-FFT) of a long period EEG signal occurred in real-time. In order to analyze efficiently EEG signals which have frequency characteristics in the range of 0 Hz to 100 Hz, a 256-point FFT processor is designed, which is based on a single-memory bank architecture and the radix-4 algorithm. The designed FFT processor has been verified by FPGA implementation, and has high accuracy with arithmetic error less than 2%.

With the pace of modern lifestyle, about 40–50 million people in the world suffer from epilepsy—a disease with neurological disorder. Electroencephalography (EEG) is the process of recording brain signals that generate due to a small amount of electric discharge in brain. This may occur due to the information flow among several neurons. Therefore, in every minute, analysis of EEG signal can solve much neurological disorders like epilepsy. In this paper, a systematic procedure for analysis and classification of EEG signal is discussed for identification of epilepsy in a human brain. The analysis of EEG signal is made through a series of steps from feature extraction to classification. Feature extraction from EEG signal is done through discrete wavelet transform (DWT), and the classification task is carried out by MLPNN based on supervised training algorithms such as backpropagation, resilient propagation (RPROP), and Manhattan update rule. Experimental study in a Java platform confirms that RPROP trained MLPNN to classify EEG signal is promising as compared to back-propagation or Manhattan update rule trained MLPNN.

Fast Fourier transform (FFT) analysis is a popular method of spectral analysis of atrial fibrillation cycle lengths (AFCL). Autocorrelation function (ACF) analysis is also available, so the aim of this study was to elucidate the relationship between FFT and ACF analyses in the spectral analysis of AFCLs. A total of 75 atrial fibrillation (AF) data from 39 patients were subjected to analysis. The dominant frequencies (DFs) from 4 different spectral resolutions of the FFT and peak AFCL from the ACF analysis were compared. In the FFT analysis using rectified signals, the DF was influenced by spectral resolution, no matter how the signals were tapered by the Hanning or Hamming window or filtered with the low-pass filter. There was a significant relationship between the DF from each spectral resolution and the peak AFCL. The DF from the 4,096-point FFT analysis had the strongest relationship to the peak AFCL with the smallest difference, when using 30-s AF data. In a study of the different lengths of the atrial fibrillation data, the DF also had a strong correlation to the peak AFCL with a small difference. The peak AFCL obtained from ACF analysis was not of the same quality as that from FFT analysis, but had the same value as the DF from FFT analysis.

The purpose of this paper is to demonstrate the capabilities of continuous wavelet transform (CWT) in analyzing electroencephalogram (EEG) signals produced through a single-electrode recording device. Further, CWT is used to evaluate standard fast Fourier transform (FFT) analysis results. Sequential resting eyes-closed (EC) and eyes-open (EO) EEG signals, recorded from individuals during a one year period (N = 25), are analyzed. The absolute and relative geometric mean powers of the EEG δ, θ, α, and β-bands are calculated using FFT and CWT analysis. A sliding Blackman window based FFT analysis shows a statistically significant α and β-band dominant peaks for EC compared to EO recordings. These results confirm well-known results reported in the literature, which validates the EEG recording device. CWT analysis using Morlet mother function results are consistent with those of FFT analysis and revealed additional differences where a second range of statistically significant dominant scales are clearly observed in the δ-band for EO compared with EC, which has not been reported in the literature. However, the difference between EO and EC power spectra in the β range is less significant in the wavelet analysis.

Systematical casing pressure measurements were undertaken to supplement instantaneous experiment data to available database of a high-speed small-scale compressor rotor, which was crucial for understanding the flow mechanism of short-length scale stall inception. At the same time, improved full-annulus simulations were conducted to assist in interpretation of experimental observations. In Part I of current investigation, FFT (fast Fourier transformation) and STFT (short time Fourier transformation) analyses of instantaneous casing pressure signals were conducted to conclude flow characteristics near casing at stable operating conditions, and reasonable explanation of experimental observations was given in combination with the current and previous numerical results. FFT analyses of casing pressure signals showed a characteristic hump with varying band lower than blade passing frequency (BPF) appeared at near-stall stable conditions. This indicated that an unsteady phenomenon emerged from the near-tip flow field for the test rotor. The variation in the amplitude of characteristic hump implied that underlying flow mechanism leading to the emergence of unsteady phenomenon originated from a location near leading edge and within passage. Further STFT analyses showed that the active frequency of this unsteady phenomenon varied with time, thus leading to the appearance of excitation band in FFT analysis results. FFT and STFT analyses of monitoring results of numerical probes arranged in absolute frame showed a similar unsteady phenomenon appeared in the simulated near-tip flow field. Detailed analyses of simulated instantaneous flow fields and comparison with measured flow characteristics indicated that the unsteady flow phenomenon observed in experiments was equivalent to rotating instability (RI) as far as non-uniform tip loading distribution was concerned, and the formation and activity of tip secondary vortex (TSV) was the flow mechanism of emergence of RI.

Depression is a common phenomenon in the present scenario. Due to the fast pace at which our lives move and immense pressure that we face adolescents, office goers and even the elders face depression. Diagnosing depression in the early curable stages is very important and may even save the life of a patient. EEG signal analysis has been used for medical research like epilepsy, sleep disorder, insomnia etc. Similarly, video signal analysis has been used for facial features detection, eye movement, emotion recognition etc. Collaborating both the methods accuracy of depression detection can be improved upon. This paper describes a novel method for combining both EEG signal analysis and facial emotion recognition through video analysis to successfully categorize depression into various levels. For this aim, power spectrum of three frequency bands (alpha, beta, and theta) and the whole bands of EEG are used as features along with standard deviation, mean and entropy.

In order to investigate the nonlinear relations of the electroencephalogram (EEG) signals under different brain functional states, higher-order statistics is used to study the nonlinear interrelation of the EEG components for the purpose of further understanding of the EEG generation and its construction. A parametric bispectral estimation for the analysis of EEG signals has been presented as an useful tool for detecting the nonlinearity of EEG signals. The bicoherence pattern is proposed in the paper to extract more. information beyond first and second-order statistics or spectral structure. Several EEG signals with normal subjects in different brain functional states are investigated by employing the non-Gaussian parametric model. The experimental results demonstrate that practical EEG signals provide obvious quadratic nonlinear coupling phenomena. The bicoherence structures of EEG signals is also different from that corresponding to the brain functional states. It is suggest that the bispectral analysis can be used as an effective way for nonlinear analysis and automatic classification of EEG signals and other biomedical measurements.

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early on, EEG analysis was restricted to visual inspection of EEG records. Since there is no definite criterion evaluated by the experts, visual analysis of EEG signals is insufficient. For example, in the case of dominant alpha activity delta and theta, activities are not noticed. Routine clinical diagnosis needs to analysis of EEG signals. Therefore, some automation and computer techniques have been used for this aim (Guler et al., 2001). Since the early days of automatic EEG processing, representations based on a Fourier transform have been most commonly applied. This approach is based on earlier observations that the EEG spectrum contains some characteristic waveforms that fall primarily within four frequency bands—delta (1–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz). Such methods have proved beneficial for various EEG characterizations, but fast Fourier transform (FFT), suffer from large noise sensitivity. Parametric power spectrum estimation methods such as AR, reduces the spectral loss problems and gives better frequency resolution. AR method has also an advantage over FFT that, it needs shorter duration data records than FFT (Zoubir et al., 1998). A powerful method was proposed in the late 1980s to perform time-scale analysis of signals: the wavelet transforms (WT). This method provides a unified framework for different techniques that have been developed for various applications. Since the WT is appropriate for analysis of non-stationary signals and this represents a major advantage over spectral analysis, it is well suited to locating transient events, which may occur during epileptic seizures. Wavelet’s feature extraction and representation properties can be used to analyse various transient events in biological signals. (Adeli et al., 2003) gave an overview of the discrete wavelet transform (DWT) developed for recognising and quantifying spikes, sharp waves and spike-waves. Wavelet transform has been used to analyze and characterise epileptiform discharges in the form of 3-Hz spike and wave complex in patients with absence seizure. Through wavelet decomposition of the EEG records, transient features are accurately captured and localised in both time and frequency context. The capability of this mathematical microscope to analyse different scales of neural rhythms is shown to be a

This paper attempts to explain some methodological issues regarding EEG signal analysis which might lead to misinterpretation and therefore to unsubstantiated conclusions. The so called “split-alpha,” a “new phenomenon” in EEG spectral analysis described lately in few papers is such a case. We have shown that spectrum feature presented as a “split alpha” can be the result of applying improper means of analysis of the spectrum of the EEG signal that did not take into account the significant properties of the applied Fast Fourier Transform (FFT) method. Analysis of the shortcomings of the FFT method applied to EEG signal such as limited duration of analyzed signal, dependence of frequency resolution on time window duration, influence of window duration and shape, overlapping and spectral leakage was performed. Our analyses of EEG data as well as simulations indicate that double alpha spectra called as “split alpha” can appear, as spurious peaks, for short signal window when the EEG signal being studied shows multiple frequencies and frequency bands. These peaks have no relation to any frequencies of the signal and are an effect of spectrum leakage. Our paper is intended to explain the reasons underlying a spectrum pattern called as a “split alpha” and give some practical indications for using spectral analysis of EEG signal that might be useful for readers and allow to avoid EEG spectrum misinterpretation in further studies and publications as well as in clinical practice.

Dyslexia is a neurological disorder which needs to be detected at an early stage to know their specific needs and to help them cope with the problem. One of the ways to detect dyslexia is by using Electroencephalogram (EEG). In this study, the EEG signals recorded from dyslexic's children while performing writing activities were analyzed. The EEG signals were recorded from 4 channels; C3, C4, P3 and P4 and filtered using band pass filter with frequency range 8 Hz to 30 Hz. The signal was analyzed using Fast Fourier Transform. Analysis of EEG signals showed that the range of frequency for dyslexic children during writing is 22–28 Hz which is considered high and indicates that they are trying hard to write a correct word.

In this paper, a method based on the nolinear Granger causality is used to analyze epilptic EEG and ECG signal.Polynomial kernel function, Gaussian kernel function and sigmoid kernel function are used to map the linear data in low dimensional input space into high dimensional feature space .In this space linear Granger method can be used to analyse the biomedical signals.The results show that the effect of ECG signals to EEG signals is more significant than that of EEG signals to ECG signals and the result by normal subjects is more significant than that of epileptic subjects.This study is helpful for the analysis of epileptic patient's EEG and ECG signal..

Gyrotrons are microwave / millimeter wave devices capable to deliver megawatt level continuous power at a frequency range up to 170GHz. The critical design issues for a high power gyrotrons are: (1) Magnetron injection Gun (2) Cavity with proper mode (3) quasi-optical launcher (4) depressed collector system and (5) RF-window. The higher order cavity modes like TE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mp</sub> (m, p >;>;2) are normally selected for high power, high frequency, long pulse Gyrotrons. These higher order modes are converted into a Gaussian beam output (TEM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">00</sub> mode) to transmit power from gyrotron with minimized diffraction losses. The quasi-optical launchers are used to convert higher order modes to a Gaussian beam. The basic quasi-optical launcher consists of a Vlasov type launcher with a helical cut in a cylindrical cavity output waveguide section and a combination of profiled mirrors. The profiled mirrors are basically phase correcting mirrors, which give desired output as Gaussian beam. Fast Fourier Transform (FFT) analysis can be performed to design phase correcting mirrors for quasi-optical launcher of gyrotron. The FFT analysis is being carried to study and design of quasi-optical mirrors for matching optic unit. The simulation is done for a Gaussian beam propagating in free space. The paper discusses about the design of quasi-optical mode converter for the Gyrotron. The FFT analysis on the propagation of a gaussian beam in free space will be presented.

AR spectral analysis of EEG signals by using maximum likelihood estimation

Nonlinear methods are better suited for analysis of EEG signals than so-called linear methods like fast Fourier transform (FFT). In this chapter, we illustrate the use of the Higuchi’s fractal dimension method. We present several examples of the usefulness of this method in application to sleep-EEG analysis, revealing influence of electromagnetic fields, monitoring anesthesia, and assessing bright light therapy (BLT) and electroconvulsive therapy (ECT). We conclude that Higuchi’s fractal dimension method is very useful in the analysis of EEG signals.