• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.2101-2101
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• 2001

Near-infrared spectroscopy is now being used in clinical diagnosis as a non-invasive monitor of tissue oxygenation state. However, due to lack of the optical pathlength information within tissues, it is still difficult to quantitate the hemoglobin concentration with present CW techniques. Time-resolved spectroscopy (TRS), which measures temporal profiles of emerging light from tissues, enables to estimate the pathlength distribution within tissues by converting time to distance. Consequently, quantitative measurement of tissue oxygenation is possible by analyzing the data with optical diffusion equation 1) or our Microscopic Beer-Lambert law2). Time-Resolved Spectroscopy System : TRS-1O3) Our TRS-10 system consists of a three-wavelength (759, 797, 833 nm) PLP as pulsed light source, a high speed PMT with high sensitivity and three signal-processing circuits for time-resolved measurement (CFD/TAC, A/D converter and histogram memory). Optical pulse train consisting of 759, 797 and 833nm is generated by PLP at 5㎒ repetition rate and irradiated a sample through a single optical fiber. The diffuse-reflected light from the sample is collected by a bundle fiber and then detected by the PMT for single photon measurement. After being amplified by a following fast amplifier, the electrical signals for each wavelength are picked out by CFD/TAC module. Then, a signal processing circuit integrated the TRS data for each wavelength individually. The simultaneous TRS measurement for three wavelengths achieved without any optical or mechanical switch. Experiment and Results Input and detection fibers of TRS-10 were attached at the human forehead with a fiber separation of 3cm. TRS measurements were continuously performed for about 20 minutes including 2 minutes hyper ventilation. It was observed that the total hemoglobin concentration was decreasing during the hyper ventilation and recovered until 2 minutes after hyper ventilation. On the other hand, the deoxy-hemoglobin concentration began to increase after hyper ventilation and had its peak at around 2 minute later, showing 502 drop from 75% to 60% due to inhibition of breathing by performing hyper ventilation. The results showed that this system might be able to quantitate the concentrations of oxy- and deoxy-hemoglobin in the human brain.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.7-10
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• 2007

많은 과학자들은 인간의 사고를 functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG)등을 이용해서 두뇌 활동 영역을 연구하고 있다. 주로 의학 분야와 심리학의 영역에서 두뇌의 활동을 연구하여 간질이나 발작을 알아내고 거짓말 탐지 분야에서도 사용된다. 본 논문에서는 사람의 두뇌활동을 측정하여 인간의 감정을 인식하는 연구에 중점을 두었다. 특히 fMRI와 TRS 그리고 EEG를 이용해서 사람의 두뇌활동을 측정하는 연구를 하였다. 많은 연구자들이 한 가지 측정 장치만을 사용하여서 측정하거나 fMRI와 EEG를 동시에 측정하는 연구를 진행하고 있다. 현재에는 단순히 두뇌의 활동을 측정하거나 측정시 발생하는 잡음들을 제거하는 연구들에 중점을 두고 진행되고 있다. 본 연구에서는 fMRI와 TRS를 동시에 측정하여 얻은 두뇌 활동 데이터를 가지고 감정에 따른 활동영역의 EEG신호를 측정하였다. EEG 신호분석에 있어서 기존의 뇌파만을 가지고 특정을 찾아내는 것을 넘어서 각각의 채널에서 기록되는 뇌파의 파형을 주파수에 따라서 분류하고 정확한 측정을 위해 낮은 주파수를 제거하고 연구자가 필요한 부분의 뇌파를 분석하였다.