• 제어로봇시스템학회논문지
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• 제19권6호
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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.

• 통합자연과학논문집
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• 제11권4호
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• pp.167-183
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• 2018

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권1호
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• pp.12-18
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• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• 대한의용생체공학회:의공학회지
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• 제41권1호
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• pp.22-27
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• 2020

Auditory stimuli are commonly used in various electroencephalogram experiments, also in EEG-based brain-computer interface systems. However, using conventional earphones that blocks the ear canal attenuates or even blocks external environmental sound which might cause loss of crucial information from surroundings. Instead, bone-conductive earphones are able to deliver sound through vibration without blocking the ear canal. To investigate the feasibility of the bone-conductive earphones for auditory-stimuli based experiments, we compared N100 event-related potential features as well the event-related spectral perturbation and inter-trial coherence of auditory steady-state response between conventional and bone-conductive earphones. The results showed no significant differences between bone conduction and conventional earphones regardless of distinct sound pressures. This result shows that bone conductive earphones can be used for auditory experiments when the environmental sound is crucial to the user.

• 전자공학회논문지 IE
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• 제43권1호
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• pp.16-24
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• 2006

본 논문에서는 생체신호의 발생을 자유롭게 조절 할 수 있는 전정기관으로부터 생성된 전기신호를 추출하여 window discriminator로 필요한 신호를 선택한 후, BCI 시스템을 적용하여 정밀하고 정확한 제어가 가능하고 멀티채널을 이용하여 데이터를 처리할 수 있는 통합 시스템을 구현하였다. 전정신경세포의 흥분신호를 검출하는 전치증폭기는 측정된 이득이 47.6dB, 왜율은 100 Hz에서 측정 시 0.005%이었으며, 입력임피던스 특성은 12M$\Omega$이었다. Window discriminator는 2개의 CPU를 사용하여 역할을 분담함으로써 처리 속도를 증가시켰고, ADC 샘플링 주파수는 87kHz이었으며, 기존 시스템보다 분해능이 2배, 변별 오차는 10배가 향상되었음을 알 수 있었다. 제안된 방법이 뇌파분석법 보다 100ms동안 축적된 데이터양이 약 100배 정도 감소되었음을 입증하였다.

• 한국지능시스템학회논문지
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• 제19권1호
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• pp.122-127
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• 2009

본 논문에서는 피험자가 A, B, C, D 글자를 말하는 상상을 할 때 사고중추에서와 Broca's area 에서 EEG 신호를 측정하였으며 이 신호를 Event-Related Spectral Perturbation (ERSP), Inter-Trial Coherence (ITC) 그리고 Event Related Potential (ERP) 방법을 통해 분석하여 보았다. 그 결과 F7, FT7 영역의 뇌파에서 각 문자를 보여주는 자극 제시 후 0$\sim$300ms 동안의 1$\sim$13Hz에서 높은 coherence를 보였으며, P300 이 뚜렷하게 나타나는 것을 확인할 수 있었다. 하지만 ERP를 통해 분석해본 결과 각 글자에 대한 차이를 구분하고자 하였던 처음 연구의 동기와 달리 각 글자를 말할 때 ERP가 약간의 차이를 보이기는 하였으나 각 문자에 대한 차이라거나 이 차이를 통해 문자를 구별할 수 있다고 하기는 어려웠다. 하지만 본 논문에서는 이 실험결과를 통해 기존에 운동관련 뇌 영역에 국한되어 있던 BCI 연구의 한계를 극복하고 보다 다양한 서비스를 제공할 수 있는 응용 시스템을 제안하였다.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.286-290
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• 2002

두뇌-컴퓨터 인터페이스(brain-computer interface)를 적용하기 위한 연구로서 주어진 문제에서 긍/부정을 선택할 때 나타나는 뇌파를 분별하기 위해서 시간-주파수 분석을 하였다. 단시간 퓨리에 변환(short time fourier transform : STFT)을 하여 긍/부정 선택시 뇌파의 시간-주파수 변화량을 보고, 시간-주파수 분해능이 좋은 웨이블릿 변환(wavelet transform)을 적용하여 서로 비교하였다. 두 가지 분석에서 공통된 결과는 주로 RT전 0.5초 주위에서 유의미한 결과를 나타내었고, 웨이블릿 분석에서 더 좁은 구간에 나타나며, 통계적으로 더 유의미한 결과를 나타내었다.

• 제어로봇시스템학회논문지
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• 제19권8호
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• 정보처리학회논문지B
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• 제16B권1호
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• pp.63-70
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• 2009

BCI 기술은 생체신호인 뇌파를 수집하여 신호처리를 거친 후 실질적인 기기제어 및 통신 시스템 등을 제어하는 시스템 관련 기술이다. BCI 시스템 구현을 위해서는 뇌파의 특성을 실시간으로 분석하여 학습 시키고 학습된 뇌파의 특성을 적용하는 단계가 요구된다. 본 논문에서는 EEG 데이터를 효율적으로 분석하기 위해 점진적으로 갱신되는 주성분 분석을 이용하여 왼손/오른손 동작에 영향을 미치는 EEG 신호의 특징을 찾고, 이를 반영하여 데이터의 차원을 축소한다. 입력 자료의 특징을 충분히 포함하면서 낮은 차원을 가지는 데이터를 이용한다면 분류를 위한 계산량을 감소시킬 수 있을 뿐만 아니라 불필요한 특징을 제거함으로써 분류 성능을 향상 시킬 수 있다. 본 논문에서는 점진적으로 갱신되는 주성분 분석을 이용하여 데이터의 차원을 축소하고 이에 대한 효율성을 검증하기 위해 K-NN분류기를 이용하여 분류 정확도 측정을 수행하였다. 그 결과 주성분 분석을 이용하여 특징을 추출하고 분류율을 측정한 경우보다 평균 5% 높은 분류 정확율을 보였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2003년도 봄 학술발표논문집 Vol.30 No.1 (B)
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• pp.178-180
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• 2003

EEG-based brain computer interface(BCI) provides a new communication channel between human brain and computer. EEG data is a multivariate time series so that hidden Markov model (HMM) might be a good choice for classification. However EEG is very noisy data and contains artifacts, so useful features mr expected to improve the performance of HMM. In this paper we addresses the usefulness of principal component features with Hidden Markov model (HHM). We show that some selected principal component features can suppress small noises and artifacts, hence improves classification performance. Experimental study for the classification of EEG data during imagination of a left, right up or down hand movement confirms the validity of our proposed method.