• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.3
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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.

• Clinical and Experimental Pediatrics
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• v.59 no.sup1
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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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• v.10 no.2
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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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.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.32 no.3
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• pp.93-98
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• 2021

Electroencephalography (EEG) measures neural activation due to various cognitive processes. EEG and event-related potentials (ERPs) are widely used in studies investigating psychopathology and neural substrates of psychiatric diseases in children and adolescents. The present study aimed to review recent ERP studies in child and adolescent psychiatry. ERPs are non-invasive methods for studying synaptic functions in the brain. ERP might be a candidate biomarker in child-adolescent psychiatry, considering its ability to reflect cognitive and behavioral functions in humans. For the EEG study of psychiatric diseases in children and adolescents, several ERP components have been used, such as mismatch negativity, P300, error-related negativity (ERN), and reward positivity (RewP). Regarding executive functions and inhibition in patients with attention-deficit/hyperactivity disorder (ADHD), P300 latency, and ERN were significantly different in patients with ADHD compared to those in the healthy population. ERN showed meaningful changes in patients with anxiety disorders, such as generalized anxiety disorder, separation anxiety disorder, and obsessive-compulsive disorder. Patients with depression showed significantly attenuated RewP compared to the healthy population, which was related to the symptoms of anhedonia.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1628-1630
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• 1997

The purpose of this study is to see the method of the analysis of EEG(Electroencephalography) whcih is a nonlinear system, to quantize human emotion under color stimulation using the analysis of EEG. The result of this study would be used clinical study and development fo image instruments with color. In this study, the method of the analysis of EEG is power spectrum using FFT(Fast Fourier Transform) and the modelling of EEG under color stimulation base on back propagation Neural Networks ond of AI(Artfical Intellignece) skills. First, input layer make a match to relative power which get analyzing s in 4 channels, and output layer make a match to color stimulation which is measured human emotion. Finally, weights of each neurons determine by learing back porpagation Neural Networks.

• International Journal of Contents
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• v.12 no.3
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• pp.12-16
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• 2016

Brain computer interfaces (BCI) usually have focused on classifying the explicitly-expressed intentions of humans. In contrast, implicit intentions should be considered to develop more intelligent systems. However, classifying implicit intention is more difficult than explicit intentions, and the difficulty severely increases for subject independent classification. In this paper, we address the subject independent classification of implicit intention based on electroencephalography (EEG) signals. Among many machine learning models, we use the support vector machine (SVM) with radial basis kernel functions to classify the EEG signals. The Fisher scores are evaluated after extracting the gamma, beta, alpha and theta band powers of the EEG signals from thirty electrodes. Since a more discriminant feature has a larger Fisher score value, the band powers of the EEG signals are presented to SVM based on the Fisher score. By training the SVM with 1-out of-9 validation, the best classification accuracy is approximately 65% with gamma and theta components.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.853-859
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• 2013

The purpose of this study was to identify the correlation between electroencephalography (EEG) and strength, using grip strength. 64-channel EEG data were recorded from five healthy subjects in tasks requiring handgrip contractions of nine levels of MVC (Maximal Voluntary Contraction). We found the ERS (Event-Related Synchronization)/ERD (Event-Related Desynchronization) at the measured EEG data using STFT (Short-Time Furier Transform) and spectral power in the EEG of each frequency range displayed in the graph. In this paper, we identified that the stronger we contracted, the greater the spectral power was increased in the ${\beta}$, ${\gamma}$ wave.

• Korean Journal of Biological Psychiatry
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• v.19 no.1
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• pp.65-69
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• 2012

Objectives : Much is still unknown about the neurophysiological mechanisms or dynamics of the sleep onset process. Detrended fluctuation analysis (DFA) is a new tool for the analysis of electroencephalography (EEG) that may give us additional information about electrophysiological changes. The purpose of this study is to analyze long-range correlations of electroencephalographic signals by DFA and their changes in the sleep onset process. Methods : Thirty channel EEG was recorded in 61 healthy subjects (male:female=34:27, age=$27.2{\pm}3.0$ years). The scaling exponents, alpha, were calculated by DFA and compared between four kinds of 30s sleep-wakefulness states such as wakefulness, transition period, early sleep, and late sleep (stage 1). These four states were selected by the distribution of alpha and theta waves in O1 and O2 electrodes. Results : The scaling exponents, alpha, were significantly different in the four states during sleep onset periods, and also varied with the thirty leads. The interaction between the sleep states and the leads was significant. The means (${\pm}$ standard deviation) of alphas for the states were 0.94 (${\pm}0.12$), 0.98 (${\pm}0.12$), 1.10 (${\pm}0.10$), 1.07 (${\pm}0.07$) in the wakefulness, transitional period, early sleep and late sleep state respectively. The mean alpha of anterior fifteen leads was greater than that of posterior fifteen leads, and the two regions showed the different pattern of changes of the alpha during the sleep onset periods. Conclusions : The characteristic findings in the sleep onset period were the increasing pattern of scaling exponent of DFA, and the pattern was slightly but significantly different between fronto-temporal and parieto-occipital regions. It suggests that the long-range correlations of EEG have a tendency of increasing from wakefulness to early sleep, but anterior and posterior brain regions have different dynamical process. DFA, one of the nonlinear analytical methods for time series, may be a useful tool for the investigation of the sleep onset period.

• Journal of Biomedical Engineering Research
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• v.39 no.1
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• pp.43-47
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• 2018

Traditional electroencephalography (EEG)-based authentication systems generally use external stimuli that require user attention and relatively long time for authentication. The aim of this study is to investigate the feasibility of biometric authentication based on EEG without using any external stimuli. Seventeen subjects took part in the experiment and their EEGs were measured while repetitively closing and opening their eyes. For identifying each subject, we calculated inter- and intra-subject cross-correlation using changes in alpha activity (8-13 Hz) during eyes closed as compared to eyes open. In order to optimize the number of recording electrodes, we calculated authentication accuracy by progressively reducing the number of electrodes used in the analysis. Significant increase in alpha activity was observed for all subjects during eyes closed, focusing on occipital areas, and spatial patterns of changed alpha activity were considerably different between the subjects. A mean authentication accuracy of 92.45% was obtained, which was retained over 75% when using only 8 electrodes placed around occipital areas. Our results could demonstrate the feasibility of the proposed novel authentication method based on resting state EEGs.