• Biomolecules & Therapeutics
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• 제25권4호
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• pp.390-395
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• 2017

The present study investigated the sedative effects of Sophora flavescens (SF) and its bioactive compound, matrine through performing locomotor activity test and the electroencephalography (EEG) analysis in the rat. The underlying neural mechanism of their beneficial effects was determined by assessing c-Fos immunoreactivity and serotonin (5-HT) in the brain utilizing immunohistochemical method and enzyme-linked immunosorbent assay. The results showed that SF and matrine administration had an effect on normalization of caffeine-induced hyperactivity and promoting a shift toward non-rapid eye movement (NREM) sleep. c-Fos-immunoreactivity and 5-HT level in the ventrolateral preoptic nucleus (VLPO), a sleep promoting region, were increased in the both SF and matrine-injected groups. In conclusion, SF and its bioactive compound, matrine alleviated caffeine-induced hyperactivity and promoted NREM sleep by activating VLPO neurons and modulating serotonergic transmission. It is suggested that SF might be a useful natural alternatives for hypnotic medicine.

• 한국소음진동공학회논문집
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• 제23권3호
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• pp.258-266
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• 2013

This paper presents the correlation between psychological and physiological acoustics for the automotive acceleration sound. The research purpose of this paper is to evaluate the sound quality of acceleration sound of a passenger car based EEG signal. The previous method for the objective evaluation of sound quality is to use sound metrics based on psychological acoustics. This method uses not only psychological acoustics but also physiological acoustics. For this work, the sounds of 7 premium passenger cars are recorded and evaluated subjectively by 33 people. The correlation between the subjective rating and sound metrics is calculated based on physiological acoustics. Finally the correlation between the subjective rating and the EEG signal measured on the brain is also calculated. Throughout these results the new evaluation system for the sound quality on the automotive acceleration sound of a passenger car has been developed based on bio-signal.

• 대한물리의학회지
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• 제13권1호
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• pp.1-9
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• 2018

PURPOSE: This study investigated the neurophysiological and behavioral adaptation during one or both hands movement in chronic stroke patients. METHODS: The study included sixteen hemiplegic stroke patients. Neurophysiological data (brain activation and muscle activation) were examined by electroencephalography (EEG) and electromyography (EMG), and behavioral adaptation was examined by wrist extension angle during wrist extension with one hand or both hands. Outcome variables of one hand or both hands were; mu rhythm of the EEG, EMG amplitude of wrist extensor and flexor muscles, and wrist angle of Myomotion 3D motion analysis. RESULTS: Our results revealed that wrist extension angle was significant increased during both hands movement compared to one hand movement (p<.05). Furthermore, in affected sensorimotor area, there was significant increase in the brain activation during both hands movement compared to one hand movement (p<.05). However, there was no significant different between one hand and both hands movement in muscle activation (p>.05). CONCLUSION: According to the findings of this experiment, bilateral arm movement improved brain activity on affected sensorimotor area and wrist extension angle. Therefore, we suggest that bilateral arm movement would positive effect on stroke rehabilitation in terms of increase in brain activation on affected motor area and wrist extension during bilateral arm movement.

• 제어로봇시스템학회논문지
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• 제19권11호
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• pp.1022-1028
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• 2013

It is well knowns that based on the CSP (Common Spatial Pattern) algorithm, the linear projection of an EEG (Electroencephalography) signal can be made to spaces that optimize the discriminant between two patterns. Sharing disadvantages from linear time invariant systems, CSP suffers from the non-stationary nature of EEGs causing the performance of the classification in a BCI (Brain-Computer Interface) system to drop significantly when comparing the training data and test data. The author has suggested a simple idea based on the parallel model of CSP filters to improve the performance of BCI systems. The model was tested with a simple CSP algorithm (without any elaborate regularizing methods) and a perceptron learning algorithm as a classifier to determine the improvement of the system. The simulation showed that the parallel model could improve classification performance by over 10% compared to conventional CSP methods.

• 대한불안의학회지
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• 제6권2호
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• pp.102-108
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• 2010

Objective : Although studies have explored responses to fear had been assessed using various psychophysiological methods, results have been inconsistent. The present study examined psychophysiological responses in healthy subjects after viewing fear stimuli in a video clip for set up future fear related psychophysiological studies. Methods : We monitored three psychophysiological variables (electroencephalography, skin temperature, and heart rate variability) in adults who watched either a control stimulus movie clip or a fear-inducing movie clip. Results : In 16 healthy adults, theta activity decreased significantly after the fear stimulus as compared to the normal stimulus. However the participants showed no differences in heart rate variability or skin temperature between the fear and normal control stimulus situations. Conclusion : In the limbic area, theta activity corresponds with information processing, integration into previous memories and long-term potentiation. In this study, we suggest decreased theta activity represents amygdalo-hippocampal activity, associated with fear, short-term memory, and memory extinction in the healthy adults. Further studies are needed to evaluate the interaction of fear, memory, and the pathophysiology of anxiety disorder in patient with anxiety disorders.

• Clinical and Experimental Pediatrics
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• 제59권sup1호
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• pp.129-132
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• 2016

Ictal tachycardia and bradycardia are common arrhythmias; however, ictal sinus pause and asystole are rare. Ictal arrhythmia is mostly reported in adults with temporal lobe epilepsy. Recently, ictal arrhythmia was recognized as a major warning sign of sudden unexpected death in epilepsy. We present an interesting case of a child with ictal sinus pause and asystole. A 27-month-old girl was hospitalized due to 5 episodes of convulsions during the past 2 days. Results of routine electroencephalography (EEG) were normal, but she experienced brief generalized tonic seizure for 3 days. During video-monitored EEG and echocardiography (ECG), she showed multiple myoclonic seizures simultaneously or independently, as well as frequent sinus pauses. After treatment with valproic acid, myoclonus and generalized tonic seizures were well controlled and only 2 sinus pauses were seen on 24-hour Holter ECG monitoring. Sinus dysfunction should be recognized on EEG, and it can sometimes be treated successfully with only antiepileptic medication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권2호
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• pp.807-824
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• 2016

In this paper, a method of mood classification based on user brainwaves is proposed for real-time application in commercial services. Unlike conventional mood analyzing systems, the proposed method focuses on classifying real-time user moods by analyzing the user's brainwaves. Applying brainwave-related research in commercial services requires two elements - robust performance and comfortable fit of. This paper proposes a filter based on Regularized Common Spatial Patterns (RCSP) and presents its use in the implementation of mood classification for a music service via a wireless consumer electroencephalography (EEG) device that has only 14 pins. Despite the use of fewer pins, the proposed system demonstrates approximately 10% point higher accuracy in mood classification, using the same dataset, compared to one of the best EEG-based mood-classification systems using a skullcap with 32 pins (EU FP7 PetaMedia project). This paper confirms the commercial viability of brainwave-based mood-classification technology. To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated. Furthermore, as a prototype service, this paper introduces a mood-based music list management system called MyMusicShuffler based on the proposed mood-classification method.

• ETRI Journal
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• 제42권2호
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• pp.217-229
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• 2020

Long-term electroencephalography (EEG) monitoring is time-consuming, and requires experts to interpret EEG signals to detect seizures in patients. In this paper, we propose a novel automated method called adaptive slope of wavelet coefficient counts over various thresholds (ASCOT) to classify patient episodes as seizure waveforms. ASCOT involves extracting the feature matrix by calculating the mean slope of wavelet coefficient counts over various thresholds in each frequency subband. We validated our method using our own database and a public database to avoid overtuning. The experimental results show that the proposed method achieved a reliable and promising accuracy in both our own database (98.93%) and the public database (99.78%). Finally, we evaluated the performance of the method considering various window sizes. In conclusion, the proposed method achieved a reliable seizure detection performance with a short-term window size. Therefore, our method can be utilized to interpret long-term EEG results and detect momentary seizure waveforms in diagnostic systems.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• 대한건축학회논문집:계획계
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• 제36권4호
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.