• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.801-804
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• 2002

뇌기능 연구 수단으로 널리 사용되고 있는 뇌파의 시각적 분석 및 정량적 분석시 오차를 증가시키는 원인이 되어 왔던 잡파(artifact)를 제거 대상이 아닌 제어 신호로써 활용한다. 본 연구에서는 다양한 잡파 중 뇌파 측정시 가장 잘 포함되고, 시각적으로 쉽게 구별이 가능한 안면근(facial muscle) 신호를 이용한다. 측정된 뇌파에 파워스펙트럼(power spectrum)을 적응하여 뇌파를 분석하고, Backpropagation 알고리즘을 이용하여 전 처리된 뇌파를 인식하는 2 채널 실시간 인식(recognition) 및 분류(classification) 시스템을 구현한다. 이와 같이 구현된 시스템을 이용하여 5 방향(상, 하, 좌, 우, 정지) 제어를 실시함으로써 뇌-컴퓨터간 통신을 통한 방향제어 시스템을 구현하였다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.396-400
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• 1999

인간의 감성은 다차원적 감정으로 이루어져 있다. 본 연구는 감성의 2차원 구조를 근거로 쾌-불쾌, 각성-이완 2차원적 감성을 생리신호로 분류하고자 하였다. 20명 남녀 대학생을 참가시켜 자극을 2차원 감성자극(쾌(펜디향수), 불쾌(에탄올), 각성(싸이렌), 이완(가요))으로 정의하고, 2*2 자극제시로 감성을 유발하였다. 26명의 남녀대학생을 실험에 참가시켜 4가지 감성을 유발하여, 측정한 생리신호로는 중추신경계의 활동을 나타내는 EEG(f3, p3, f4, p4)를 측정하였으며, 자율신경계의 활동을 나타내는 ECG(lead II), GSR, SKT를 측정하였다. 각각의 측정한 신호들에 대한 t-test를 실시하여 유의성 있는 변수를 추출하였으며 추출된 변수는 EEG의 f3(beta), p3(delta, beta), f4(delta), p4(alpha), HRV의 HF, HF/LF, GSR의 rising time이었으며 2차원 감성을 분류하였다.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1406-1417
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• 2020

Sounds that have been evaluated to be effective in inducing sleep are helpful to reduce sleep disorders. Generally, several sounds have been verified the effects by brainwave experiments, but it cannot be considered on all users because of individual variation for effects. Moreover, the effectiveness for inducing sleep is not known for all new sounds made by creative activities. Therefore, new classification system is required to collect new effective sounds with considering personal brainwave characteristics. In this paper, we propose a new sound classification method by applying improved MinHash cluster to brain waves. The proposed method will classify them through whether it is effective for sleep care by evaluation his brainwave during listening for each sound. In order to prove effectiveness of the proposed classification method, we conducted accuracy experiment for sleep sound classification using verified sleep induction sound. In addition, we have compared time for existing method and proposed method. The former is scored 85% accuracy in the experiment. We confirmed the latter one that the average processing time was reduced to 70%. It is expected to be one of method for pre-screening whether it is effective when a new sound is introduced as a sound for sleep induction.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.527-533
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• 2013

BCI (Brain-Computer Interface) is a system that transforms a subject's brain signal related to their intention into a control signal by classifying EEG (electroencephalograph) signals obtained during the imagination of movement of a subject's limbs. The BCI system allows us to control machines such as robot arms or wheelchairs only by imaging limbs. With the exact same experiment environment, activated brain regions of each subjects are totally different. In that case, a simple approach is to use as many channels as possible when measuring brain signals. However the problem is that using many channels also causes other problems. When applying a CSP (Common Spatial Pattern), which is an EEG extraction method, many channels cause an overfitting problem, and in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest an optimal channel selection method using a BPSO (Binary Particle Swarm Optimization), BPSO with channel impact factor, and GA. This paper examined optimal selected channels among all channels using three optimization methods and compared the classification accuracy and the number of selected channels between BPSO, BPSO with channel impact factor, and GA by SVM (Support Vector Machine). The result showed that BPSO with channel impact factor selected 2 fewer channels and even improved accuracy by 10.17~11.34% compared with BPSO and GA.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.277-283
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• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• 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 the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2349-2356
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• 2008

In this paper, we present the method to effectively extract and classify the EEG caused by only brain activity when a normal subject is in a state of mental activity. We measure the synchronous EEG on the auditory event when a subject who is in a normal state thinks of a specific task, and then shift the baseline and reduce the effect of biological artifacts on the measured EEG. Finally we extract only the mental task signal by averaging method, and then perform the recognition of the extracted mental task signal by computing the AR coefficients. In the experiment, the auditory stimulus is used as an event and the EEG was recorded from the three channel $C_3-A_1$, $C_4-A_2$ and $P_Z-A_1$. After averaging 16 times for each channel output, we extracted the features of specific mental tasks by modeling the output as 12th order AR coefficients. We used total 36th order coefficient as an input parameter of the neural network and measured the training data 50 times per each task. With data not used for training, the rate of task recognition is 34-92 percent on the two tasks, and 38-54 percent on the four tasks.

• Journal of Computing Science and Engineering
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• v.7 no.2
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• pp.132-138
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• 2013

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.03a
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• pp.281-284
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• 1999

본 연구는 인간의 뇌파로부터 간단한 의사 표시를 식별하는 기술을 얻어 뇌파인터페이스를 구현하기 위한 기초연구로서 수행되었다. 실험에 참가한 피험자들은 컴퓨터 화면에 나타나는 문제를 본 후 답을 제시받았을 때 이것이 옳은지, 그른지에 대한 양분법적 판단반응을 해야하며, 이때 동시에 뇌파가 기록되었다. 옳다는 긍정반응과, 옳지 않다는 부정반응시의 뇌파를 비교한 결과 전두엽 부위의 fp1, f3, f4 부위에서 부정의 대답을 할 경우 theta파와 fast alpha파의 상대적 출현량이 긍정의 경우에 비하여 통계적으로 유의하게 컸다.