• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.113-116
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• 1994

Estimation of the evoked potential using the iterated bispectrum and cross-correlation (IBC) has been tried for both simulation and real clinical data. Conventional time average (TA) method suffers from synchronization error when the latency time of the evoked potential is random, which results in poor SNR distortion in the estimation of EP waveform. Instead of EP signal average in time domain, bispectrum is used which is insensitive to time delay. The EP signal is recovered by the inverse transform of the Fourier amplitude and phase obtained from the bispectrum. The distribution of the latency time is calculated using cross-correlation between EP signal estimated by the bispectrum and the acquired signal. For the simulation. EEG noise was added to the known EP signal and the EP signal was estimated by both the conventional technique and bispectrum technique. The proposed bispectrum technique estimates EP signal more accurately than the conventional technique with respect to the maximum amplitude of a signal, full width at half maximum(FWHM). signal-to-noise-ratio, and the position of maximum peak. When applied to the real visual evoked potential(VEP) signal. bispectrum technique was able to estimate EP signal more distinctively. The distribution of the latency time may play an important role in medical diagonosis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2B
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• pp.232-243
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• 2010

In this paper, various physiological signals of humans were measured and analyzed to inference their psychological state and biological information, and Bio-Signal Context aware system (BSC), which recognizes the current context of its users as well as the information of exterior environment and offers the service appropriate for them, was designed and implemented. The BSC extracts and analyzes the features from bio-signals, such as the measured electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR), with its different sensors, has the input of the analyzed results, and discriminates four psychological states of rest, concentration, tension and melancholy. In addition to the results of the discriminated psychological states, the information of biological condition analyzed from the user's bio-signals, for example, heart rate variability (HRV), Galvanic skin response (GSR) and body temperature, and the information of external environment related to the user's are collected to offer the service fit for the user's present biological condition by inferring and recognizing the user's present situation.

• Journal of the HCI Society of Korea
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• v.9 no.1
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• pp.11-21
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• 2014

We present an experimental framework to analyze the physical reactions and causal factors of Visually Induced Motion Sickness (VIMS) using electroencephalography (EEG) signals and vital signs. We studied eleven subjects who are voluntarily participated in the experiments and conducted online and offline surveys. In order to simulate videos including global motions that could cause the motion sickness, we extracted global motions by optical flow estimation method from hand-held captured video recordings containing intense motions. Then, we applied the extracted global motions to our test videos with action movies and texts. Each genre of video includes three levels of different motions depending on its intensity. EEG signal and vital sign that were measured by a portable electrocorticography device and an electronic monometer in real time while the subjects watch the videos including ones with the extracted motions. We perform an analysis of the EEG signals using Distance Map(DM) calculated by correlation among each channel of brain signal. Analysis using the vital signs and the survey results is also performed to obtain relationship between the VIMS and causal factors. As a result, we clustered subjects into three groups based on the analysis of the physical reaction using the DM and the correlation between vital sign and survey results, which shows high relationships between the VIMS and the intensity of motions.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.429-434
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• 2012

This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(${\mu}8$~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.

• Journal of IKEEE
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• v.24 no.2
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• pp.654-660
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• 2020

Through the output of brain waves during muscle operation, this paper checks whether it is possible to find characteristic vectors of brain waves that are capable of dividing left and right movements by extracting brain waves in specific areas of muscle signal output that include the motion of the left and right muscles or the will of the user within EEG signals, where uncertainties exist considerably. A typical surface EMG and noninvasive brain wave extraction method does not exist to distinguish whether the signal is a motion through the degree of ionization by internal neurotransmitter and the magnitude of electrical conductivity. In the case of joint and motor control through normal robot control systems or electrical signals, signals that can be controlled by the transmission and feedback control of specific signals can be identified. However, the human body lacks evidence to find the exact protocols between the brain and the muscles. Therefore, in this paper, efficiency is verified by utilizing the results of application of CSP (Common Spatial Pattern) filter to verify that the left-hand and right-hand signals can be extracted through brainwave analysis when the subject's behavior is performed. In addition, we propose ways to obtain data through experimental design for verification, to verify the change in results with or without filter application, and to increase the accuracy of the classification.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.90-95
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• 2014

The researchs using brain-computer interface, the new interface system which connect human to macine, have been maded to implement the user-assistance devices for control of wheelchairs or input the characters. In recent researches, there are several trials to implement the speech recognitions system based on the brain wave and attempt to silent communication. In this paper, we studied how to extract features of vowel based on international phonetic alphabet (IPA), as a foundation step for implementing of speech recognition system based on electroencephalogram (EEG). We conducted the 2 step experiments with three healthy male subjects, and first step was speaking imagery with single vowel and second step was imagery with successive two vowels. We selected 32 channels, which include frontal lobe related to thinking and temporal lobe related to speech function, among acquired 64 channels. Eigen value of the signal was used for feature vector and support vector machine (SVM) was used for classification. As a result of first step, we should use over than 10th order of feature vector to analyze the EEG signal of speech and if we used 11th order feature vector, the highest average classification rate was 95.63 % in classification between /a/ and /o/, the lowest average classification rate was 86.85 % with /a/ and /u/. In the second step of the experiments, we studied the difference of speech imaginary signals between single and successive two vowels.

• Progress in Medical Physics
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• v.5 no.2
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• pp.35-43
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• 1994

Electromagnetic wave may induce effect and damage on the bio-body, either by electric fields of magnetic fields. We measure electrophysiological changs in rabbit's brain exposed to 2.45GHz micro wave(power density 40mW/cm$^2$) which distance 30cm from the source. In order to process the bio-electrical signal (EEG), used pre-amplifier module with self-made and Digtal analyzer computer system. Spectal analysis of the EEG showed variable power in the frequency range(1～30Hz) through each exposure time(10min, 20min, 30min) before and after. In effectively measured by the bio-electrical signal processing and can found threshold of minmal permissible exposure and lethal exposure.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.