• 한국운동역학회지
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• 제34권2호
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• pp.71-80
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• 2024

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (f_CoP, f_AJtorque and f_EEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using f_EEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

• 한국컴퓨터정보학회논문지
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• 제28권10호
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• pp.133-153
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• 2023

본 논문에서 우리는 뇌 신호 측정 기술 중 하나인 뇌전도를 활용한 새로운 접근방식을 제안한다. 전통적으로 연구자들은 감정 상태의 분류성능을 향상시키기 위해 뇌전도 신호와 생체신호를 결합해왔다. 우리의 목표는 뇌전도와 결합된 생체신호의 상호작용 효과를 탐구하고, 뇌전도+생체신호의 조합이 뇌전도 단독사용 또는 임의로 생성된 의사 무작위 신호와 결합한 경우에 비해 감정 상태의 분류 정확도를 향상시킬 수 있는지를 확인한다. 네 가지 특징추출 방법을 사용하여 두 개의 공개 데이터셋에서 얻은 데이터 기반의 뇌전도, 뇌전도+생체신호, 뇌전도+생체신호+무작위신호, 및 뇌전도+무작위신호의 네 가지 조합을 조사했다. 감정 상태 (작업 대 휴식 상태)는 서포트 벡터 머신과 장단기 기억망 분류기를 사용하여 분류했다. 우리의 결과는 가장 높은 정확도를 가진 서포트 벡터 머신과 고속 퓨리에 변환을 사용할 때 뇌전도+생체신호의 평균 오류율이 뇌전도+무작위신호와 뇌전도 단독 신호만을 사용한 경우에 비해 각각 4.7% 및 6.5% 높았음을 보여주었다. 우리는 또한 다양한 무작위 신호를 결합하여 뇌전도+생체신호의 오류율을 철저하게 분석했다. 뇌전도+생체신호+무작위신호의 오류율 패턴은 초기에는 깊은 이중 감소 현상으로 인해 감소하다가 차원의 저주로 인해 증가하는 V자 모양을 나타냈다. 결과적으로, 우리의 연구 결과는 뇌파와 생체신호의 결합이 항상 유망한 분류성능을 보장할 수 없음을 시사한다.

• 전기전자학회논문지
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• 제12권4호
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• pp.204-210
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• 2008

EEG 신호 중, 알파파는 안정시에 우세하게 나타나며 베타파는 흥분시에 우세하게 나타나는 것으로 알려져 있다. 또한 동양의 한의학에서는 상대적으로 길고 고른 호흡일 때가 짧고 변화가 심한 호흡일 때 보다 안정된 상태를 나타낸다고 알려져 있다. 본 연구에서는 EEG의 안정상태를 정량적으로 나타내기 위한 뇌파의 정량화 지표와 호흡의 안정상태를 정량적으로 나타내기 위한 호흡 정량화 지표를 정의하여 안정상태에 있어서 EEG와 호흡의 연관성을 찾아내고자 하였다. 총 20명의 피험자에 대해 각각 20분간의 실험을 통해 본 연구의 유효성을 검증하였다.

• 대한의용생체공학회:의공학회지
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• 제30권1호
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• pp.10-17
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• 2009

Accurate electroencephalography (EEG) forward calculation is of importance for the accurate estimation of neuronal electrical sources. Conventional studies concerning the EEG forward problems have investigated various factors influencing the forward solution accuracy, e.g. tissue conductivity values in head compartments, anisotropic conductivity distribution of a head model, tessellation patterns of boundary element models, the number of elements used for boundary/finite element method (BEM/FEM), and so on. In the present paper, we investigated the influence of modeling errors in the boundary element volume conductor models upon the accuracy of the EEG forward solutions. From our simulation results, we could confirm that accurate construction of boundary element models is one of the key factors in obtaining accurate EEG forward solutions from BEM. Among three boundaries (scalp, outer skull, and inner skull boundary), the solution errors originated from the modeling error in the scalp boundary were most significant. We found that the nonuniform error distribution on the scalp surface is closely related to the electrode configuration and the error distributions on the outer and inner skull boundaries have statistically meaningful similarity to the curvature distributions of the boundary surfaces. Our simulation results also demonstrated that the accumulation of small modeling errors could lead to considerable errors in the EEG source localization. It is expected that our finding can be a useful reference in generating boundary element head models.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.747-752
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• 2011

HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.

• 융합신호처리학회논문지
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• 제5권4호
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• pp.263-270
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• 2004

최근 웨이블릿 변환은 많은 분야에서 다양하게 적용된다. 본 논문에서 tasks뇌파의 중요한 몇가지 특성파 검출을 위한 다비치 웨이블릿은 뇌파분석에 필요하다. 우리가 제안한 시스템은 다른 방법보다는 특성파 검출에 높은 성능을 가졌다. 본 연구의 뉴럴시스템의 구조는 하나의 은닉층과 3계층 피드포워드층은 오류 BP 학습알고리즘을 적용하였다. 4명의 피험자에게 알고리즘을 적용하여 92% 분류율을 보였다. 제안된 시스템은 웨이블릿과 신경망으로 tasks 뇌파의 보다 정확하게 분석함을 보였다. 모의실험결과 tasks 뇌파는 의사의 노동력을 줄일수 있고 정량적 해석이 가능함을 보였다.

• Biomolecules & Therapeutics
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• 제7권3호
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• pp.242-248
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• 1999

Diazepam, a benzodiazepine (BDZ) agonist, produces sedation and flumazenil, a BDZ antagonist, blocks these actions. The aim of this study was to examine the effects of BDZs on cortical electroencephalogram (EEG) in rats. The recording electrodes were implanted over the frontal and parietal cortices bilaterally, and the reference and ground electrodes over cerebellum under ketamine anesthesia. To assess the effects of diazepam and flumazenil, rats were injected with diazepam (1 mgHg, i.p.) and/or flumazenil ( 1 mg/kg, i.p.), and the EEG was recorded before and after drugs. Normal awake had theta peak in the spectrum and low amplitude waves, while normal sleep showed large amplitude of slow waves. The powers of delta, theta and alpha bands were increased during sleep compared with during awake. Diazepam reduced the mobility of the rat and induced sleep with intermittent fast spindles and large amplitude of slow activity, and it produced broad peak over betaL band and increased the power of gamma band, which were different from EEG patterns in normal sleep. Saline injection awakened rats and abolished fast spindles for a short period about 2-5 min from EEG pattern during diazepam-induced sleep. Flumazenil blocked both diazepam-induced sleep and decreased the slow activities of delta, theta, alpha and betaL, but not of gamma activity for about 10 min or more. This study may indicate that decrease in power of betaL and betaH bands can be used as the measure of central action of benzodiazepines, and that the EEG parameters of benzodiazepines have to be measured without control over the behavioral state by experimenter.

• Clinical and Experimental Pediatrics
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• 제56권12호
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• pp.534-539
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• 2013

Purpose: Electroencephalography (EEG) is frequently ordered for patients with febrile seizures despite its unclear diagnostic value. We evaluated the prevalence of abnormal EEGs, the association between clinical findings and abnormal EEGs, and the predictive value of EEG for the recurrence of febrile seizures Methods: Data were collected on 230 children who were treated for febrile seizures at Kyung Hee University Medical Center from 2005 to 2009. EEGs were recorded after 1-2 days of hospitalization when children became afebrile. EEG patterns were categorized as normal, epileptiform, or nonspecific relative to abnormalities. The patients' medical records were reviewed, and telephone interviews with the families of the children were conducted to inquire about seizure recurrence. The relationships between clinical variables, including seizure recurrence, and EEG abnormalities were evaluated. Results: Of the 131 children included, 103 had simple and 28 had complex febrile seizures. EEG abnormalities were found in 41 children (31%). EEG abnormalities were more common in children with complex than simple febrile seizures (43% vs. 28%), but the difference was not statistically significant. Logistical regression analysis showed that having multiple seizures in a 24-hour period was significantly predictive of abnormal EEG (odds ratio, 2.98; 95% confidence interval, 1.0 to 88; P =0.048). The frequency of recurrence did not differ significantly in the normal (31%) and abnormal (23%) EEG groups. Conclusion: Multiple seizures within 24 hours were predictive of abnormal EEG in children with febrile seizures. Abnormal EEG was not predictive of febrile seizure recurrence.

• 전자공학회논문지SC
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• 제46권2호
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• pp.41-46
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• 2009

본 연구는 두피에서 발생하는 EEG(Electroencephalogram)신호를 측정한 후 두뇌활동과 관련된 지표 중 집중도를 추출하여 집중도의 크기에 따라 하드웨어를 제어하는 집중도 무선전송 시스템을 구연하고자 하였다. 뇌파신호를 측정하여 집중도를 추출하기 위해 두피의 좌, 우 두 채널을 사용하였으며 Biopac의 MP100과 EEG100C을 이용하여 뇌파신호 계측 증폭 및 필터링을 하였다. 계측된 EEG 신호로부터 특정 주파수 대역 및 스펙트럼을 분석하기 위해서 LabVIEW 8.5를 이용하여 FFT(Fast Fourier Transformation)처리를 하였다. 이를 통해 SMR파, Mid-Bata파, Theta파 주파수영역으로 분류 한 후 집중도 추출 알고리즘을 적용하여 집중도 지표를 추출하였고 추출된 집중도 신호를 무선전송하여 BCI(Brain Computer Interface)기술에 응용하고자 레고 자동차에 적용하여 보았다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(5)
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• pp.131-133
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• 2000

In this paper, we proposed a new method for localizing the independent sources generating the observed EEG based on independent component analysis (ICA). The performance of the algorithm was tested through computer simulations.