• Physical Therapy Rehabilitation Science
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• 제6권4호
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• pp.164-169
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• 2017

Objective: This research investigated the effects of simple visual imagery and mental rotation imagery on neural activity of adults who are at high risk of smart phone addiction by measuring their electroencephalography (EEG). Design: Cross-sectional study. Methods: Thirty people with a high risk of smart phone addiction was selected and then were evaluated for their neural activation patterns using EEG after reminding them about simple visual imagery and mental rotation imagery. A simple visual image was applied for 20 seconds using a smartphone. This was followed by a resting period of 20 seconds. Mental rotation imagery was applied for 20 seconds. During mental rotation imagery, the rotational angle was selected at random. We compared activation patterns according to the analyzed EEG with hemisphere reminding them about imagery. Results: On the EEG, theta rhythm from the left hemisphere parietal area increased when the subjects were reminded of mental rotation imagery, and sensorimotor rhythm from close to the left hemisphere area increased when the subjects were reminded of simple visual imagery. Conclusions: Neural activation from the left hemisphere occurs for motor imagery in adults who are at high risk of smart phone addiction. These results identify a neural mechanism of adults who a have high risk of smart phone addiction, which may provide contribute to the development of motor rehabilitation for smartphone users.

• 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.

• 한국지능시스템학회논문지
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• 제22권6호
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• pp.793-798
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• 2012

In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.

• 대한의용생체공학회:의공학회지
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• 제32권4호
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• pp.295-304
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• 2011

In this study, we evaluated the EEG based BCI algorithm using common spatial pattern to find realistic applicability using neurofeedback EEG based BCI algorithm - EEG mode, feature vector calculation, the number of selected channels, 3 types of classifier, window size is evaluated for 10 subjects. The experimental results have been evaluated depending on conditioned experiment whether neurofeedback is used or not In case of using neurofeedback, a few subjects presented exceptional but general tendency presented the performance improvement Through this study, we found a motivation of development for the specific classifier based BCI system and the assessment evaluation system. We proposed a need for an optimized algorithm applicable to the robust motor imagery evaluation system with more useful functionalities.

• Journal of Computing Science and Engineering
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• 제7권2호
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• pp.139-146
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• 2013

Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even to restore their motor disabilities. This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reported on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients. Both types of studies are important as the former advances the technology of BCI for stroke, and the latter demonstrates the clinical efficacy of BCI in stroke. Finally some challenges are discussed.

• 전자공학회논문지
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• 제51권5호
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• pp.197-206
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• 2014

운동심상(Motor imagery) 기반의 뇌-컴퓨터 인터페이스(Brain-computer Interface)는 주로 뇌전도(Electroencephalography, EEG)를 이용하여 사용자의 자발적인 운동 의지를 읽는 기술로 최근 주목받고 있다. 이 중에서도 피실험자의 운동 의지를 정확히 해석하기 위해 감각운동 영역(sensorimotor area)의 일부분에서 나타나는 ${\mu}$-대역(8-13Hz)의 전위 감소 현상인 event related desynchronization(ERD)을 분석하는 연구가 많이 진행되고 있다. 하지만 EEG는 공간 해상도가 낮고 사용자에 따라 ERD가 발생하는 주파수 대역이 다소 차이가 있어 추정에 어려움이 있다. 이에 대한 개선 방법의 하나로서 공간 필터를 구현하는 common spatial pattern (CSP)과 필터 뱅크(filter bank)를 결합한 형태인 discriminative filter bank common spatial pattern(DFBCSP)이 제안되었다. 그러나 DFBCSP는 EEG 신호의 평균 전력(power)의 Fisher ratio를 이용하여 사용자에 따른 효과적인 주파수 대역을 포함하는 discriminative filter bank(DFB)를 구성하여 분류 정확도를 향상시켰지만 ERD의 공간 패턴이 나타나는 적절한 필터를 선택하지 않는 경우가 발생한다. 이러한 문제를 해결하기 위해 본 논문에서는 EEG 신호의 평균전력 대신 CSP의 특성 벡터를 이용하여 DFB를 구성하는 방법을 제안한다. 기존의 방법과 제안한 방법의 필터 선택 결과와 분류 정확도 분석을 통해 CSP 특성 벡터가 DFB 구성에 더욱 효과적임을 보인다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.2(A)
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• pp.21-22
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• 2010

Brain-Computer Interface (BCI) 는 뇌 신호를 이용하여 생각으로 기계 및 컴퓨터를 제어 할 수 있는 기술이다. 뇌전도(Electroencephalography, EEG) 를 이용한 본 연구는 왼쪽/오른쪽 손 상상 움직임 실험에 대해서 특징 추출 (feature extraction)에 관�� 연구로 총 9명의 피험자로부터 얻어진 뇌 전도 데이터를 이용하여 전통적인 방법 (Common Spatial Pattern, CSP 및 Fisher Linear Discriminant, FLDA)을 이용해 구한 분류 정확도와 본 논문에서 사용 된 Symbolic transfer entropy (STE)을 통해 얻어진 특징에 대한 결과를 보여 준다. 본 연구를 통하여 STE를 통한 특징 추출 방법이 의미가 있다고 생각한다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2011년도 한국컴퓨터종합학술대회논문집 Vol.38 No.1(A)
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• pp.469-472
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• 2011

신체 부위를 움직이는 상상을 할 때, 일반적으로 뇌의 감각 및 운동 피질 영역에서 특정 주파수 대역의 EEG(Electroencephalography) 신호의 세기가 감소하거나 증가하는 ERD(Event-Related Desynchronization)/ERS(Event-Related Synchronization) 현상이 발생한다. 하지만 ERD/ERS는 현상은 피험자에 의존적이고 매시도마다 큰 차이를 보인다. 이러한 문제를 해결하기 위해, 본 논문에서 각 시간-주파수 공간에 대하여 서로 다른 공간 필터를 구성하는 비 동질(non-homogeneous) 공간 필터 최적화 방법을 제안한다. EEG 신호는 시간에 대하여 비정상적(non-stationary) 특징을 가지기 때문에 제안하는 방법과 같이 시간에 따라 변화하는 ERD/ERS 특징을 반영하여 공간적 특징을 추출하는 방법은 시간에 대한 변화를 고려하지 않은 기존의 방법보다 우수한 성능을 보인다. 본 논문에서는 International BCI Competition IV에서 제공하는 4가지 동작 상상(왼손, 오른손, 발, 혀)에 대한 EEG 신호 데이터를 사용하여 동작 상상 분류 실험을 하고 이 결과를 기존의 타 방법들과 비교 분석하였다. 실험 결과, 피험자에 따라 서로 다른 시간-주파수 특징이 추출됨을 확인하였고, 최적화된 공간 필터들이 시간에 따라 변화하는 것을 확인하였다. 또한 이러한 특징을 이용하여 분류를 수행하였을 때, 더욱 우수한 분류 결과를 보임을 확인하였다.

• Physical Therapy Rehabilitation Science
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• 제4권1호
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• pp.28-31
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• 2015

Objective: Psychomotor imagery has been widely used to improve motor performance and motor learning. Recent research suggests that during visualization, changes occur in neurophysiological networks that make physical practice more effective in configuring functional networks for skillful behaviors. The aim of our pilot study was to determine if there was change and to what extent there was differentiation in modulation in electroencephalography (EEG) frequencies between visualizing a state of rest and a state of exercise performance and to identify the preponderant frequency. Design: Quasi-experimental design uncontrolled before and after study. Methods: EEG brain wave activity was recorded from 0-40 Hz from nine cerebral cortical scalp regions F3, Fz, F4, C3, Cz, C4, P3, POz, and P4 with a wireless telemetric EEG system. The subjects, while sitting on a chair with eyes closed, were asked to visualize themselves in a state of routine rest/relaxation and after a period of time in a state of their routine exercise performance. Results: The gamma frequency, 31-40 Hz, (${\gamma}$) was the predominant wave band in differentiation between visualizing a state of rest versus visualizing a state of exercise performance. Conclusions: We suggest these preliminarily findings show the EEG electrocortical activity for athletes is differentially modulated during visualization of exercise performance in comparison to rest with a predominant ${\gamma}$ wave band frequency observed during the state of exercise. Further controlled experimental studies will be performed to elaborate these observations and delineate the significance to optimization of psychomotor exercise performance.