• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.5
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• pp.791-796
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• 2013

Advent of the brain wave health care system is considered as an important issues in the industrial and research area in these days. It is necessary to detect EEG signals in real-time in order to support the medical emergency service for the epileptic or brain infarct patients. Since the efficient network support is an essential factor for the system, several topologies using sensor node based wireless body area network is suggested and simulated in this paper. Finally the Opnet simulation result is evaluated for the efficient topology of the body area network.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.2
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• pp.41-46
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• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We develop concentration wireless transmission system for controlling hardware by using this signal. Two channels are used for measuring EEG signal on front head and Biopac system with MP100 and EEG100C was used for measuring EEG signal, amplifying and filtering the signal. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the measured EEG signals. As a result, SMR wave, Mid-Bata wave, $\Theta$ wave classified. We extracted the concentration index by adapting concentration extraction algorithm. This concentration uldex was transferred into logo automobile device by wireless module and applied for BCI application.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.155-156
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• 2008

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We develop concentration wireless transmission system for controlling hardware by using this signal. Two channels are used for measuring EEG signal on front head and Biopac system with MP-100 and EEG100C was used for measuring EEG signal, amplifying and filtering the signal. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the measure EEG signal. As a result, ${\alpha}$ wave, ${\beta}$ wave, ${\theta}$ wave and ${\delta}$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into lego automobile device by wireless module and applied for BCI application.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.145-147
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• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We have developed concentration wireless transmission system by displaying this EEG signal on PDA mobile device. The front head was used for measuring EEG signal and INA128 with TL084 and analog elements was used for measuring EEG signal, amplifying and filtering the signal. Measured analog EEG signals changed into digital signals by using ADC of PIC24FJ192 with 10bit resolution and 500Ks/s sampling rate. So The changed digital signals have transmitted to the PDA by using bluetooth. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the transferred EEG signal. As a result, $\alpha$ wave, $\beta$ wave, $\theta$ wave and $\delta$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into PDA by wireless module and displaying.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.40-47
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• 2013

To verify the condition of patients moving in the medical center like hospital needs to be consider the various wireless communication network protocols and network components. Wireless communication protocols such as the 802.11a, 802.11g, and direct sequence has their specific characteristics, and the various components such as the number of mobile nodes or the distance of transmission range could affects the performance of the network. Especially, the network topologies are considered the characteristic of the brain wave(EEG) since the condition of patient is detected from it. Therefore, in this paper, various wireless communication networks are designed and simulated with Opnet simulator, then evaluated the performance to verify the wireless network that transmits the patient's EEG data efficiently. Overall, the 802.11g had the best performance for the wireless network environment that transmits the EEG data. However, there were minor difference on the performance result depends on the components of the topologies.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.

• 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 Audiology & Otology
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• v.24 no.1
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• pp.10-16
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• 2020

Background and Objectives:The blockage of adenosine receptors by caffeine changes the levels of neurotransmitters. These receptors are present in all parts of the body, including the auditory and vestibular systems. This study aimed to evaluate the effect of caffeine on evoked potentials using auditory brainstem responses (ABRs) and cervical vestibular-evoked myogenic potentials (cVEMPs) in a double-blind placebo-controlled study. Subjects and Methods: Forty individuals (20 females and 20 males; aged 18-25 years) were randomly assigned to two groups: the test group (consuming 3 mg/kg pure caffeine powder with little sugar and dry milk in 100 mL of water), and the placebo group (consuming only sugar and dry milk in 100 mL water as placebo). The cVEMPs and ABRs were recorded before and after caffeine or placebo intake. Results: A significant difference was observed in the absolute latencies of I and III (p<0.010), and V (p<0.001) and in the inter-peak latencies of III-V and I-V (p<0.001) of ABRs wave. In contrast, no significant difference was found in cVEMP parameters (P13 and N23 latency, threshold, P13-N23 amplitude, and amplitude ratio). The mean amplitudes of P13-N23 showed an increase after caffeine ingestion. However, this was not significant compared with the placebo group (p>0.050). Conclusions: It seems that the extent of caffeine's effects varies for differently evoked potentials. Latency reduction in ABRs indicates that caffeine improves transmission in the central brain auditory pathways. However, different effects of caffeine on auditory- and vestibular-evoked potentials could be attributed to the differences in sensitivities of the ABR and cVEMP tests.

• Korean Journal of Audiology
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• v.24 no.1
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• pp.10-16
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• 2020

Background and Objectives:The blockage of adenosine receptors by caffeine changes the levels of neurotransmitters. These receptors are present in all parts of the body, including the auditory and vestibular systems. This study aimed to evaluate the effect of caffeine on evoked potentials using auditory brainstem responses (ABRs) and cervical vestibular-evoked myogenic potentials (cVEMPs) in a double-blind placebo-controlled study. Subjects and Methods: Forty individuals (20 females and 20 males; aged 18-25 years) were randomly assigned to two groups: the test group (consuming 3 mg/kg pure caffeine powder with little sugar and dry milk in 100 mL of water), and the placebo group (consuming only sugar and dry milk in 100 mL water as placebo). The cVEMPs and ABRs were recorded before and after caffeine or placebo intake. Results: A significant difference was observed in the absolute latencies of I and III (p<0.010), and V (p<0.001) and in the inter-peak latencies of III-V and I-V (p<0.001) of ABRs wave. In contrast, no significant difference was found in cVEMP parameters (P13 and N23 latency, threshold, P13-N23 amplitude, and amplitude ratio). The mean amplitudes of P13-N23 showed an increase after caffeine ingestion. However, this was not significant compared with the placebo group (p>0.050). Conclusions: It seems that the extent of caffeine's effects varies for differently evoked potentials. Latency reduction in ABRs indicates that caffeine improves transmission in the central brain auditory pathways. However, different effects of caffeine on auditory- and vestibular-evoked potentials could be attributed to the differences in sensitivities of the ABR and cVEMP tests.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.177-186
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• 2018

The mechanism of emotion is complex and influenced by a variety of factors, so that it is crucial to analyze emotion in broad and diversified perspectives. In this study, we classified neutral and negative emotions(sadness, fear, surprise) using arousal evaluation, which is one of the psychological evaluation scales, as well as physiological signals. We have not only revealed the difference between physiological signals coupled to the emotions, but also assessed how accurate these emotions can be classified by our emotional recognizer based on neural network algorithm. A total of 146 participants(mean age $20.1{\pm}4.0$, male 41%) were emotionally stimulated while their physiological signals of the electrocardiogram, blood flow, and dermal activity were recorded. In addition, the participants evaluated their psychological states on the emotional rating scale in response to the emotional stimuli. Heart rate(HR), standard deviation(SDNN), blood flow(BVP), pulse wave transmission time(PTT), skin conduction level(SCL) and skin conduction response(SCR) were calculated before and after the emotional stimulation. As a result, the difference between physiological responses was verified corresponding to the emotions, and the highest emotion classification performance of 86.9% was obtained using the combined analysis of arousal and physiological features. This study suggests that negative emotion can be categorized by psychological and physiological evaluation along with the application of machine learning algorithm, which can contribute to the science and technology of detecting human emotion.