• 수면정신생리
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• 제15권1호
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• pp.5-11
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• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1995년도 추계학술대회
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• pp.278-281
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• 1995

In this paper, we have developed EEG (electroencephalography) analyzer for monitoring the condition of brain in neurological surgery. This system is composed of EEG amplifier. personal-computer and BSP (Digital Signal Processor). By parallel processing of DSP, this system can analysis the power spectral density change of EEG in real-time and display the CSA(Compressed Spectral Array) and CDSA(Color Density Spectral array) of EEG. This system was tested by real EEG and showed the change of EEG.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• 융합신호처리학회논문지
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• 제20권2호
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• pp.63-69
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• 2019

본 연구에서는 뇌파의 스펙트럼 분석에 의해 추출되는 마취심도 지표인 SEF(spectral edge freqency), MF(median frequency)의 가변성 감소를 위하여 뇌파의 비선형성에 근거하여 바이스펙트럼 분석기법을 도입하고자 한다. 수술환경에서 뇌파의 계측과 분석은 다양한 외부 아티팩트 요소를 감안하여야 한다. 바이스펙트럼 분석은 비선형적 신호의 특성을 추출하는 분석법으로 외부 유입 아티팩트의 유무를 확인 할 수 있어 뇌파에 인입되어 분석에 영향을 끼치는 아티팩트를 효과적으로 제거하는데 기여한다. 이러한 과정을 통해 SEF, MF와 같은 마취심도 파라미터의 실시간 가변성을 감소시킬 수 있었다. 이러한 가변성 감소는 수술현장에서 실시간 활용 가능한 임상 지표서 SEF, MF의 유용성을 제고시켜 줄 수 있을 것이다.

• The Journal of Advanced Prosthodontics
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• 제8권3호
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• pp.187-193
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• 2016

PURPOSE. This pilot study was to find the influence of complete denture on the brain activity and cognitive function of edentulous patients measured through Electroencephalogram (EEG) signals. MATERIALS AND METHODS. The study recruited 20 patients aged from 50 to 60 years requiring complete dentures with inclusion and exclusion criteria. The brain function and cognitive function were analyzed with a mental state questionnaire and a 15-minute analysis of power spectral density of EEG alpha waves. The analysis included edentulous phase and post denture insertion adaptive phase, each done before and after chewing. The results obtained were statistically evaluated. RESULTS. Power Spectral Density (PSD) values increased from edentulous phase to post denture insertion adaption phase. The data were grouped as edentulous phase before chewing (EEG p1-0.0064), edentulous phase after chewing (EEG p2-0.0073), post denture insertion adaptive phase before chewing (EEG p3-0.0077), and post denture insertion adaptive phase after chewing (EEG p4-0.0096). The acquired values were statistically analyzed using paired t-test, which showed statistically significant results (P<.05). CONCLUSION. This pilot study showed functional improvement in brain function of edentulous patients with complete dentures rehabilitation.

• The Korean Journal of Physiology and Pharmacology
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• 제28권3호
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• pp.253-264
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• 2024

Chronic neuropathic pain (CNP) is a complex condition often arising from neural maladaptation after nerve injury. Understanding CNP complications involves the intricate interplay between brain-heart dynamics, assessed through quantitative electroencephalogram (qEEG) and heart rate variability (HRV). However, insights into their interaction in chronic pain are limited. Resting EEG and simultaneous electrocardiogram (lead II) of the participants were recorded for qEEG and HRV analysis. Correlations between HRV and qEEG parameters were calculated and compared with age, sex, and body mass index (BMI)-matched controls. CNP patients showed reduced HRV and significant increases in qEEG power spectral densities within delta, theta, and beta frequency ranges. A positive correlation was found between low frequency/high frequency (LF/HF) ratio in HRV analysis and theta, alpha, and beta frequency bands in qEEG among CNP patients. However, no significant correlation was observed between parasympathetic indices and theta, beta bands in qEEG within CNP group, unlike age, sex, and BMI-matched healthy controls. CNP patients display significant HRV reductions and distinctive qEEG patterns. While healthy controls exhibit significant correlations between parasympathetic HRV parameters and qEEG spectral densities, these relationships are diminished or absent in CNP individuals. LF/HF ratio, reflecting sympathovagal balance, correlates significantly with qEEG frequency bands (theta, alpha, beta), illuminating autonomic dysregulation in CNP. These findings emphasize the intricate brain-heart interplay in chronic pain, warranting further exploration.

• 한국임상수의학회지
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• 제23권1호
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• pp.31-35
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• 2006

본 연구는 건강한 12마리의 슈나우져견에서 정량적 뇌파검사를 이용하여 진정을 평가한 것이다. 뇌파검사는 뇌나 행동의 변화와 관련된 중추신경계의 변화를 객관적으로 측정하는 데 사용이 된다. 특히 정량적 뇌파검사 방법인 spectral edge frequency 95 (SEF 95)는 진정의 상태를 평가하는 효과적인 방법이다. 본 실험에서 뇌파 전극은 8곳의 각각 다른 부위의 피하에 장착 하였으며 뇌파의 원래 파형과 SEF 95로 변환된 수치를 획득하여 분석하였다. 기전이 다른 5종의 진정제를 투여 한 후 측정된 모든 실험군의 SEF 95 값이 진정제 투여 전 상태와 비교하였을 때, 유의적으로 감소한다는 것을 확인하였다. 이상의 결과로 SEF 95의 정량적인 뇌파검사는 개에서 진정 상태를 평가하는 효과적인 방법이라고 생각된다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1990년도 추계학술대회
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• 인지과학
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• 제20권2호
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• pp.155-181
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• 2009

인간의 정신 작용을 신경 생리학적으로 연구할 때에, 심리적 현상에 수반되는 뇌파 신호에 종종 관심을 갖게 된다. 예를 들어, EEG 신호가 어떤 심리적 사건과 관련하여 시간에 따라 어떻게 변화하는 지에 관심이 있다면, 두피에 부착된 각각의 전극에서 모아지는 뇌파 신호의 파형이 실험 조건에 따라서 시간적으로 어떻게 변화하는 지를 살펴보면 된다. 이처럼 제시된 사건에 수반되어 반복된 실험적 시행의 평균을 통해 얻어진 뇌파 전위를 '사건 관련전위(ERP)'라고 한다. 뇌파는 이와 같이 전통적으로 시간 영역에서 분석할 수 있는데, 여기에 덧붙여 주파수 영역에서도 분석할 수 있다. 신호 분석법의 발달로 주파수축 분석 방법이 뇌파 분석에도 응용되고, 그 결과 뇌파 신호의 주파수 성분과 인지적 해석이 종종 의미 있는 상관성을 보인다. 이런 상황에서, 뇌파의 시간축 분석에 비하여, 주파수축 분석이 아직까지는 충분히 일반화되지 않았고, 관련 인지 과학 분야 연구자들에게 기본적인 개념을 소개하고 이해를 도울 필요가 있다고 생각되어 본 해설 논문을 준비했다. 이에, 본 해설 논문을 통해, 뇌파 신호의 주파수축 분석에 대한 기본적인 개념(예, 위상-고정)과 그 대표적인 분석방법(예, 웨이블릿 변환)을 이해하고, 뇌파의 주파수 대역별 인지적 속성에 대해서도 전반적으로 살펴보고자 한다. 나아가, 뇌에서 서로 다른 위치에 있는 전극들 간의 뇌파 신호들의 위상의 상호 관계 연구를 통해, 뇌의 기능적 연결성 연구를 이해하고자 한다.

• The Korean Journal of Physiology and Pharmacology
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• 제23권2호
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• pp.131-139
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• 2019

Manually reviewing electroencephalograms (EEGs) is labor-intensive and demands automated seizure detection systems. To construct an efficient and robust event detector for experimental seizures from continuous EEG monitoring, we combined spectral analysis and deep neural networks. A deep neural network was trained to discriminate periodograms of 5-sec EEG segments from annotated convulsive seizures and the pre- and post-EEG segments. To use the entire EEG for training, a second network was trained with non-seizure EEGs that were misclassified as seizures by the first network. By sequentially applying the dual deep neural networks and simple pre- and post-processing, our autodetector identified all seizure events in 4,272 h of test EEG traces, with only 6 false positive events, corresponding to 100% sensitivity and 98% positive predictive value. Moreover, with pre-processing to reduce the computational burden, scanning and classifying 8,977 h of training and test EEG datasets took only 2.28 h with a personal computer. These results demonstrate that combining a basic feature extractor with dual deep neural networks and rule-based pre- and post-processing can detect convulsive seizures with great accuracy and low computational burden, highlighting the feasibility of our automated seizure detection algorithm.