• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.311-313
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• 1992

Automated analysis of EEG invariably requires the inclusion of a signal conditioning filter. This paper investigates the EEG waveform distortions caused by the transient responses of the various types of signal conditioning filters. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.178-184
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• 2007

The EEG signal in general can be categorized as the Alpha wave, the Beta wave, the Theta wave, and the Delta wave. The alpha wave, showed in stable state, is the dominant wave for a human EEG and the beta wave displays the excited state. The subject of this paper was to recognize the stable state of EEG quantitatively using wavelet transform and power spectrum analysis. We decomposed EEG signal into the alpha wave and the beta wave in the process of wavelet transform, and calculated each power spectrum of EEG signal, using Fast Fourier Transform. And then we calculated the stable state quantitatively by stable state ratio, defined as the power spectrum of the alpha wave over that of the beta wave. The study showed that it took more than 10 minutes to reach the stable state from the normal activity in 69 % of the subjects, 5 -10 minutes in 9%, and less than 5 minutes in 16 %.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2548-2554
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• 2010

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%.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.829-833
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• 1992

This paper presents the design of an automated EEG analyzing system. The design considerations including processing speed, A/D conversion, filtering, and waveforms detection, are overviewed with the description of the associated EEG characteristics. The architecture of the currently implemented system consists of a p-controller based front-end signal processing unit and a host computer system. The data acquisition procedures are described along with a couple of illustrations of the acquired EEG/EOG signal.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.81-87
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• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.442-450
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• 2002

In measuring EEG, which is widely used for studying brain function, EEG is frequently mixed with noise and artifact. In this study, the signals relevant to the artifact were distracted by applying ICA to EEG signal. First, each independent component which was assumed to be the source was separated by applying ICA to EEG which involved artifact relevant to the eye movement of a normal person. Next, the signal which was assumed to be artifact was removed from the separated 18 independent components, and the nonlinear analysis method such as correlation dimension and the Iyapunov exponent was applied to each reconstructed EEG signal and the original signal including artifact in order to find meaningful difference between the two signals and infer the anatomical localization of its source and distribution. This study shows it is possible not only to analyze the brain function visually and spatially for visually complex EEG signal, but also to observe its meaningful change through the quantitative analysis of EEG by means of the nonlinear analysis.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.88-91
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• 2010

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 based on single-memory bank architecture and radix-4 algorithm is designed. The designed FFT processor has high accuracy with arithmetic error less than 3%.