• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1258-1270
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• 2017

'Semantic gap' has been a year-old problem in automatic video summarization, which refers to the gap between semantics implied in video summarization algorithms and what people actually infer from watching videos. Using the external EEG bio-feedback obtained from video watchers as a solution of this semantic gap problem has several another issues: First, how to define and measure noises against ERP waveforms as signals. Second, whether individual differences among subjects in terms of noise and SNR for conventional ERP studies using still images captured from videos are the same with those differently conceptualized and measured from videos. Third, whether individual differences of subjects by noise and SNR levels help to detect topic-irrelevant shots as signals which are not matched with subject's own semantic topical expectations (mis-match negativity at around 400m after stimulus on-sets). The result of repeated measures ANOVA test clearly shows a 2-way interaction effect between topic-relevance and noise level, implying that subjects of low noise level for video watching session are sensitive to topic-irrelevant visual shots, while showing another 3-way interaction among topic-relevance, noise and SNR levels, implying that subjects of high noise level are sensitive to topic-irrelevant visual shots only if they are of low SNR level.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.38-42
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• 1999

문명의 발달과 함께 인간은 사회생활의 증가와 부족한 수면으로 인한 스트레스와 병이 증가하고 있다. 따라서 수면에 대한 관심이 증가하면서 편안하고 쾌적한 수면을 위한 수면환경에 대한 연구가 진행되어지고 있다. 본 연구는 쾌적한 온열환경 제시를 위한 방법으로서 여름철 실내환경이 수면에 미치는 영향을 알아보기 위해 22$^{\circ}C$, 26$^{\circ}C$, 3$0^{\circ}C$의 3가지 온도조건을 제시하고 5명의 피험자를 대상으로 수면다원검사를 실시하여 EEG, EOG, ECG, EMG 등의 생리신호를 측정하였다. 측정된 생리신호를 통해 수면단계분석과 수면효율을 분석한 결과 총 수면시간, SWS latency, 총 수면시간에 대한 SWS 시간의 비율이 26$^{\circ}C$의 조건에서 가장 좋은 결과를 나타내었으며, 22$^{\circ}C$, 3$0^{\circ}C$의 순서로 나타났다. 이러한 분석을 통해 온도차에 따라 수면상태가 달라짐을 관찰할 수 있었고, 여름철에 26$^{\circ}C$ 정도의 실내온도가 편안하고 쾌적한 수면을 위한 실내온열환경임을 알 수 있었다.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.13-19
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• 2021

While watching the movie, the audience shows various emotional reactions. Emotional reactions such as sadness and anger, joy and anger appear depending on the storyline of the film. This aspect can be seen through the audience's brain wave response. This study is to examine the relationship between the movie story development and the movie story development through brain wave measurement of the emotional reaction of the audience in situations and events occurring in the movie development. Four American films, which represent each genre and are well known to many people, were selected for the study. These are of the adventure genre, of the animation genre, of the action genre, and of the drama genre. In order to measure the emotional response of these movies, four cases were set centered on the PPG of EEG and analyzed as a time series graph pattern. It can be seen that the emotional response on the graph has a certain relationship with the story development. It is expected that this study will help in selecting a genre when making a movie in the future, especially when deciding how to compose and develop a story, and it will help to induce the emotions of the audience.

• Journal of the Korea Computer Graphics Society
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• v.25 no.2
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• pp.19-29
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• 2019

We develop a complete system that includes data collection, signal processing, and real-time interaction for effective neurofeedback training. Our system supports a sophisticated technique to find threshold values which are quite important for effective neurofeedback system. A therapist specifies a target success rate of positive feedback, allowable error and time. The system computes a current success rate and compare it with the target one. If the difference between two rates exceeds the allowable error for allowable time, we find an optimum threshold value to obtain the target success rate by using numerical optimization technique. We conduct several experiments by varying input parameters: target success rate, allowable error and time, and demonstrate the effectiveness of our technique by showing the desired target success rate is stably obtained and systematically controlled by input parameters.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.174-180
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• 2022

Emotion recognition is an essential component of complete interaction between human and machine. The issues related to emotion recognition are a result of the different types of emotions expressed in several forms such as visual, sound, and physiological signal. Recent advancements in the field show that combined modalities, such as visual, voice and electroencephalography signals, lead to better result compared to the use of single modalities separately. Previous studies have explored the use of multiple modalities for accurate predictions of emotion; however the number of studies regarding real-time implementation is limited because of the difficulty in simultaneously implementing multiple modalities of emotion recognition. In this study, we proposed an emotion recognition system for real-time emotion recognition implementation. Our model was built with a multithreading block that enables the implementation of each modality using separate threads for continuous synchronization. First, we separately achieved emotion recognition for each modality before enabling the use of the multithreaded system. To verify the correctness of the results, we compared the performance accuracy of unimodal and multimodal emotion recognitions in real-time. The experimental results showed real-time user emotion recognition of the proposed model. In addition, the effectiveness of the multimodalities for emotion recognition was observed. Our multimodal model was able to obtain an accuracy of 80.1% as compared to the unimodality, which obtained accuracies of 70.9, 54.3, and 63.1%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.227-236
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• 2007

Ubiquitous systems have grown explosively over the few years. Nowadays users' needs for high qualify service lead a various type of user terminals. One of various type of user interface, various types of effective human computer interface methods have been developed. In many researches, researchers have focused on using brain-wave interface, that is to say, BCI. Nowadays, researches which are related to BCI are under way to find out effective methods. But, most researches which are related to BCI are not centralized and not systematic. These problems brought about ineffective results of researches. In most researches related in HCI, that is to say - pattern recognition, the most important foundation of the research is to build correct and sufficient DB. But there is no effective and reliable standard research conditions when researchers are gathering brain-wave in BCI. Subjects as well as researchers do not know effective methods for gathering DB. Researchers do not know how to instruct subjects and subjects also do not know how to follow researchers' instruction. To solve these kinds of problems, we propose effective brain-wave DB building system using the five senses stimulation. Researcher instructs the subject to use the five senses. Subjects imagine the instructed senses. It is also possible for researchers to distinguish whether brain-wave is right or not. In real time, researches verify gathered brain-wane data using spectrogram. To verify effectiveness of our proposed system, we analyze the spectrogram of gathered brain-wave DB and pattern. On the basis of spectrogram and pattern analysis, we propose an effective brain-wave DB building method using the five senses stimulation.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.238-244
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• 2019

In this study, we compared and analyzed the performance of each Convolution Neural Network (CNN) by implementing the CNN that reflected the characteristics of the cerebral structure, in order to analyze the CNN that was used for the prediction of cybersickness, and provided the performance varying depending on characteristics of the brain. Dizziness has many causes, but the most severe symptoms are considered attributable to vestibular dysfunction associated with the brain. Brain waves serve as indicators showing the state of brain activities, and tend to exhibit differences depending on external stimulation and cerebral activities. Changes in brain waves being caused by external stimuli and cerebral activities have been proved by many studies and experiments, including the thesis of Martijn E. Wokke, Tony Ro, published in 2019. Based on such correlation, we analyzed brain wave data collected from dizziness-inducing environments and implemented the dizziness predictive artificial neural network reflecting characteristics of the cerebral structure. The results of this study are expected to provide a basis for achieving optimal performance of the CNN used in the prediction of dizziness, and for predicting and preventing the occurrence of dizziness under various virtual reality (VR) environments.

• Clinical and Experimental Pediatrics
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• v.48 no.11
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• pp.1225-1231
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• 2005

Purpose : This study compares the first epileptic seizures between preterm and term-born children with periventricular leukomalacia and epilepsy. Methods : From 108 cases having lesions of high signal intensity around the ventricles in T2 weighted imaging of a brain magnetic resonance study, we selected 37 cases that showed epileptic seizures two times or more and divided them into the group of preterm-born(27 cases) and term-born children(10 cases). A retrospective study was made by comparing the two groups with regard to age, type of the first epileptic seizures, EEG findings and responsiveness to anticonvulsants. Results : The age of the first epileptic seizure was $22.2{\pm}18.3$ months in the preterm-born group and $26.9{\pm}21.1$ months in the term-born group(P=0.505). As for the first epileptic seizure, 11 out of the 27 cases in the preterm-born group had infantile spasms. Out of the 10 cases in the term-born group, 7 had complex partial seizures. In the preterm group, hypsarrhythmias were found in 11 cases, focal epileptiform discharges in 6 cases. In term-born group, focal epileptiform discharges were found in 5 cases but no epileptiform discharge was found in 3 cases. Intractable epilepsies were diagnosed in 6 cases and all of them belonged to the preterm-born group. Conclusion : More severe epilepsies such as infantile spasm and intractable epilepsies seem to be more common in preterm-born epileptic children with PVL as well as more severely abnormal EEG finding compared to term-born epileptic children.