• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.240-243
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• 2013

본 논문에서는 로봇 제어 목적의 응용을 위해 SVM 알고리즘과 HMM 알고리즘을 근간으로 하는 효과적인 뇌파 데이터 자동 분류 방법을 제안한다. Emotive Epoc 헤드셋 뇌파 측정 장비를 이용하여 뇌파 데이터를 수집하고, 수집된 뇌파 데이터로부터 FFT알고리즘을 이용하여 특징 추출을 수행한다. 그리고 SVM 알고리즘을 이용한 1단계 분류 방법과 SVM 알고리즘의 분류 결과를 다시 입력 시퀀스로 삼아 시계열 학습 알고리즘인 HMM에 적용하는 2단계 분류 방법의 실험 결과를 소개한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.517-518
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• 2023

최근 뇌-컴퓨터 인터페이스 분야에서는 뇌파 신호를 이용한 운동 심상 분류 연구가 활발히 이루어지고 있다. 뇌파는 개인별 차이가 큰 생체 신호로, 사용자에 독립적인 경우 추론이 어려워지는 문제가 있어 운동 심상 분류에서는 주로 피험자 종속적인 연구가 행해져 왔다. 본 논문에서는 컨볼루션 신경망 기반의 뇌파 분류 모델인 EEGNet 에 새로운 방식으로 개선한 Squeeze-and-Excitation block 을 적용해 피험자에 대해 독립적인 운동 심상 분류 성능을 향상시키는 방법을 제안하며, 제안한 Squeeze-and-Excitation block 을 적용한 모델이 기존 모델보다 높은 분류 성능을 보여주는 것을 실험적으로 확인하였다.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.375-381
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• 2022

Brain-computer interface (BCI) is a technology that allows users with motor disturbance to control machines by brainwaves without a physical controller. Motor imagery (MI)-BCI is one of the popular BCI techniques, but it needs a long calibration time for users to perform a mental task that causes high fatigue to the users. MI is reported as showing a similar neural mechanism as motor execution (ME) and motor observation (MO). However, integrative investigations of these three tasks are rarely conducted. In this study, we propose a new paradigm that incorporates three tasks (MI, ME, and MO) and conducted a comparative analysis. For this study, we collected Electroencephalograms (EEG) of motor imagery/execution/observation from 28 healthy subjects and investigated alpha event-related (de)synchronization (ERD/ERS) and classification accuracy (left vs. right motor tasks). As result, we observed ERD and ERS in MI, MO and ME although the timing is different across tasks. In addition, the MI showed strong ERD on the contralateral hemisphere, while the MO showed strong ERD on the ipsilateral side. In the classification analysis using a Riemannian geometry-based classifier, we obtained classification accuracies as MO (66.34%), MI (60.06%) and ME (58.57%). We conclude that there are similarities and differences in fundamental neural mechanisms across the three motor tasks and that these results could be used to advance the current MI-BCI further by incorporating data from ME and MO.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.27-34
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• 2010

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.

• Journal of Korean Neurosurgical Society
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• v.40 no.5
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• pp.323-329
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• 2006

Objective : This study was designed to analyze surgical strategies for patients with intractable supplementary sensorimotor area[SSMA] seizures. Methods : Seventeen patients who had surgical treatment were reviewed retrospectively. Preoperatively, phase I [non-invasive] and phase II [invasive] evaluation methods for epilepsy surgery were done. Seizure outcome was assessed with Engel's classification. The mean follow-up period was 27.2 months [from 12 months to 54 months]. Results : An MRI identified structural abnormality in eight patients and 3D-surface rendering revealed abnormal gyration in three. PET, SPECT, and surface EEG could not delineate the epileptogenic zone. Video-EEG monitoring with a subdural grid or depth electrodes verified the epileptogenic zone in all patients. Surgical procedures consisted of a resection of the SSMA and simultaneous callosotomy in two patients, a resection of the SSMA extending to the adjacent area in seven, a resection of a different area without a SSMA resection in seven, and a callosotomy in one. Seizure outcomes were class I in 11 [65%]. class II in five [29%], class III in one [6%]. Conclusion : In patients with intractable SSMA seizure, surgery was an excellent treatment modality. Precise delineation of the epileptogenic zone based on multimodal diagnostic methods can provide good surgical outcomes without neurological complications.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.35-43
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• 2023

Emotion recognition while driving is an essential task to prevent accidents. Furthermore, in the era of autonomous driving, automobiles are the subject of mobility, requiring more emotional communication with drivers, and the emotion recognition market is gradually spreading. Accordingly, in this research plan, the driver's emotions are classified into seven categories using psychological and behavioral data, which are relatively easy to collect. The latent vectors extracted through the auto-encoder model were also used as features in this classification model, confirming that this affected performance improvement. Furthermore, it also confirmed that the performance was improved when using the framework presented in this paper compared to when the existing EEG data were included. Finally, 81% of the driver's emotion classification accuracy and 80% of F1-Score were achieved only through psychological, personal information, and behavioral data.

• Proceedings of the ESK Conference
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• 1996.10a
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• pp.47-55
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• 1996

This study developed a comprehensive and easily applicable nuclear reactor control system evaluation method using reactor operators behavioral and mental workload database. A proposed control panel design cycle consists of the 5 steps: (1) finding out inconvenient, erroneous, and mentally stressful factors for the proposed design through evaluative experiments, (2) drafting improved design alternatives considering detective factors found out in the step (1), (3) comparative experiements for the design alternatives, (4) selecting a best design alternative, (5) returning to the step (1) and repeating the design cycle. Reactor operators behavioral and mental workload database collected from evaluative experiments in the step (1) and comparative experiments in the step (3) of the design cycle have a key roll in finding out defective factors and yielding the criteria for selection of the proposed reactor control systems. The behavioral database was designed to include the major informations about reactor operators' control behaviors: beginning time of operations, involved displays, classification of observational behaviors, dehaviors, decisions, involved control devices, classification of control behaviors, communications, emotional status, opinions for man-machine interface, and system event log. The database for mental workload scored from various physiological variables-EEG, EOG, ECG, and respir- ation pattern-was developed to indicate the most stressful situation during reactor control operations and to give hints for defective design factors. An experimental test for the evaluation method applied to the Compact Nuclear Simulator (CNS) installed in Korea Atomic Energy Research Institute (KAERI) suggested that some defective design factors of analog indicators should be improved and that automatization of power control to a target level would give relaxation to the subject operators in stressful situation.

• Sleep Medicine and Psychophysiology
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• v.18 no.2
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• pp.63-66
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• 2011

It has been controversial whether upper airway resistance syndrome (UARS) is a distinct syndrome or not since it was reported in 1993. The International Classification of Sleep Disorders classified UARS under obstructive sleep apnea syndrome (OSAS) in 2005. UARS can be diagnosed when the apnea-hypopnea index (AHI) is fewer than 5 events per hour, the simultaneously calculated respiratory disturbance index (RDI) is more than 5 events per hour due to abnormal non-apneic non-hypopneic respiratory events accompanying respiratory effort related arousals (RERAs), and oxygen saturation is greater than 92% at termination of an abnormal breathing event. Although esophageal pressure measurement remains the gold standard for detecting subtle breathing abnormality other than hypopnea and apnea, nasal pressure transducer has been most commonly used. RERAs include phase A2 of cyclical alternating patterns (CAPs) associated with EEG changes. Symptoms of OSAS can overlap with UARS, but chronic insomnia tends to be more common in UARS than in OSAS and clinical symptoms similar with functional somatic syndrome are also more common in UARS. In this journal, diagnostic and clinical differences between UARS and OSAS are reviewed.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.24-30
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• 2019

Modern people argue that mental health care is necessary because of various factors such as unstable income and conflict with others. Recently, equipments capable of measuring electrocardiogram (ECG) in wearable equipment have been widely used. In the case of overseas, it can be seen as a medical assistant [14]. By using such functions, studies are being conducted to distinguish representative emotions (joy, sadness, anger, etc.) with objective values. However, most studies are increasing accuracy by collecting complex bio-signals in a limited environment. Therefore, we examine the factors that have the greatest influence on the change and discrimination of biometric information on each stimulus.