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

• Journal of the Korean Society of Radiology
• /
• v.17 no.7
• /
• pp.1179-1187
• /
• 2023

Recent advancements in modern transportation have led to the active development of various biomedical signal and medical imaging technologies. Particularly, in the field of cognitive/neuroscience, the importance of electroencephalography (EEG) measurement and the development of accurate EEG measurement technology in moving vehicles represent a challenging area. This study aims to extensively investigate and analyze the trends in technology research utilizing EEG during driving. For this purpose, the Scopus database was used to explore EEG-related research conducted since the year 2000, resulting in the selection of about 40 papers. This paper sheds light on the current trends and future directions in signal processing technology, EEG measurement device development, and in-vehicle driver state monitoring technology. Additionally, a ultra compact 32-channel EEG measurement module was designed. By implementing it simply and measuring and analyzing EEG signals, in-vehicle EEG module's functionality was checked. This research anticipates that the technology for measuring and analyzing biometric signals during driving will contribute to driver care and health monitoring in the era of autonomous vehicles.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.293-295
• /
• 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 charateristics. The architecture of the currently implemented system consists of a -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.

• Proceedings of the KIEE Conference
• /
• 1993.11a
• /
• pp.338-341
• /
• 1993

A EEG(ElectroEncephaloGram) compression scheme based on subband coding techniques is presented in this paper. Considering the frequency characteristics of EEG, the raw signal was decomposed into different frequency bands. After decomposition, optimal bit allocation was done by adapting to the standard deviation in each frequency bands, and decomposed signals were quantized using pdf(probability density function)-optimized nonuniform quantizer. Based on the above mentioned coding scheme, coding results of various multichannel EEG signal were shown with compression ratio and SNR(signal-to-noise ratio).

• Journal of Biomedical Engineering Research
• /
• v.13 no.2
• /
• pp.125-132
• /
• 1992

Background EEG signals can be represented as the sum of a conventional AR process and an innovation process. It Is know that conventional estimation techniques, such as least square estimates (LSE) or Gaussian maximum likelihood estimates (MLE-G ) are optimal when the innovation process satisfies the Gaussian or presumed distribution. When the data are contaminated by outliers, however, these assumptions are not met and the power spectrum estimated by conventional estimation techniques may be fatally biased. EEG signal may be affected by artifacts, which are outliers in the statistical term. So the robust filtering estimation technique is used against those artifacts and it performs well for the contaminated EEG signal.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.4
• /
• pp.366-372
• /
• 2002

EEG is the electrical signal, which is occurred during information processing in the brain. These EEG signal are measured by non-invasive method. EEG has many useful information for brain activity, but artifacts which are included in EEG prevents EEG analysis, so many efforts are devoted to remove these artifacts in EEG. However, this study is going to analysis the feature of the EEG mixed with artifacts in forward-looking way, by using this way, we have found the possibility that is actually applicable to system such as control system. We have made feature difference after the linear as well as nonlinear analysis regarding EEG including typical artifacts, eye-blinking, eye rolling, muscle, and so forth.

• Proceedings of the KOSOMBE Conference
• /
• v.1992 no.05
• /
• pp.184-185
• /
• 1992

This paper investigates the EEG waveform distortions caused by the transient responses of the various types of signal conditioning filters, which are generally introduced for the automated EEG analysis. This study explicitly simulates the filter responses to the typical EEG waveform models, and compares the distortions.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1050-1057
• /
• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.11 no.9
• /
• pp.923-928
• /
• 2016

The various technologies related to the monitoring human health in real-time for the emergency situations are developing these days. Mostly the human pulse is used for measuring as the vital signs so far, but the EEG became a major research trend now. However, there are some problems measuring and sending EEG signals of all the people walking down the street to the dedicated server. Especially, there are some restrictions for collecting and sending EEG signals in 2-dimensional space in real-time. Therefore, I suggests an efficient network model using 3-dimensional space of drones to avoid the restrictions. The models are designed, simulated, and evaluated with the Opnet simulator.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.63-69
• /
• 2019

In this study, bispectrum analysis method introduced to reduce variability of SEF(spectral edge frequency) and MF(median frequency), which are the anesthetic depth indexes extracted by EEG spectral analysis. Bispectrum analysis is an analytical method that can confirm the nonlinearity of EEG. Signal measurement and analysis in the surgical environment should take into consideration various external artifact factors. Bispectrum analysis can confirm the presence of externally introduced artifacts, thereby effectively eliminating artifacts that affect the EEG signal. By applying bispectrum parameters, real-time variability of the anesthetic depth parameters SEF, MF could be reduced. Elimination of variability makes it possible to use SEF, MF as a real-time index during surgery.