• 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.

• The Journal of Korean Physical Therapy
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• 제25권5호
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• pp.273-281
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• 2013

Purpose: The purpose of this study is to determine the effect of motor imagery training on residual upper extremity strength and activities of daily living of chronic cervical spinal cord injury patients. Methods: Twelve ASIA A B patients, who had more than a 12-month duration of illness and C5 or 6 motor nerve injury level, were randomly divided into experimental group (n=6) and control group (n=6). Patients in the experimental group performed motor imagery training for five minutes prior to general muscle strengthening training, while those in the control group performed general muscle strengthening training only. The training was performed five times per week, 30 minutes per day, for a period of four weeks. General muscle strengthening training consisted of a progressive resistive exercise for residual upper extremity. Motor imagery training consisted of imagining this task performance. Before and after the training, EMG activity using BTS Pocket Electromyography and Spinal Cord Independent Measure III(SCIM III) were compared and analyzed. Results: The residual upper extremity muscle strengths showed improvement in both groups after training. Comparison of muscle strength improvement between the two groups showed a statistically significant improvement in the experimental group compared to the control group (p<0.05). SCIM III measurements showed significant improvement in the scores for Self-care and Transfer items in the experimental group. Conclusion: Motor imagery training was more effective than general muscle strengthening training in improving the residual upper extremity muscle strength and activities of daily living of patients with chronic cervical spinal cord injury.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2009년도 학술대회
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• pp.642-645
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• 한국지능시스템학회논문지
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• 제24권6호
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• pp.609-614
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• 2014

소뇌 운동실조는 점차 진행되는 신경퇴행질병이며 운동 조절을 위한 기능의 상실을 동반하기에 환자의 삶을 심각하게 저하시킨다. 소뇌 운동실조 환자는 운동제어 과정에서 부적절한 폐회로 소뇌 반응으로 인해 운동 명령이 제한된다. 본 논문에서는 최근 뇌-컴퓨터 인터페이스 기술을 이용하여 소뇌의 이상으로 인한 운동실조 환자들이 외부기기를 제어할 수 있도록 운동상상 기반의 뇌파의 특성을 분석하고 이를 이용한 뇌-컴퓨터 인터페이스 기법을 제안한다. 뇌파 기반의 뇌-컴퓨터 인터페이스의 효용성을 검증하기 위하여 소뇌 운동실조 환자와 정상인 그룹에서 운동상상에 따른 뮤밴드 파워를 조절하는 능력을 비교하였다. 이를 통하여 소뇌 운동실조 환자에의 뇌-컴퓨터 인터페이스의 가능성을 보여준다.

• 제어로봇시스템학회논문지
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• 제19권3호
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• pp.227-232
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• 2013

BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response characteristics, such as P300, SSVEP (Steady-State Visually Evoked Potential) and motor imagery. However, in order to control the robot agent without any constraint and precisely, it should take advantage of not only a signal response characteristic, but also combination. In this paper, we try to use the fusion of motor imagery and P300 from EEG for practical use of robot control in BCI environment. The results of experiments are confirmed that the recognition rate decreases compared with the case of using one kind of features, whereas it is able to classify each both characteristics and the practical use technology based on mobile robot and wireless BCI measurement system is implemented.

• 제어로봇시스템학회논문지
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• 제17권8호
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• pp.747-752
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• 2011

HMMs (Hidden Markov Models) are widely used for biological signal, such as EEG (electroencephalogram) sequence, analysis because of their ability to incorporate sequential information in their structure. A recent trends of research are going after the biological interpretable HMMs, and we need to control the complexity of the HMM so that it has good generalization performance. So, an automatic means of optimizing the structure of HMMs would be highly desirable. In this paper, we described a procedure of classification of motor imagery EEG signals using HMM. The motor imagery related EEG signals recorded from subjects performing left, right hand and foots motor imagery. And the proposed a method that was focus on the validation of the HSA (Harmony Search Algorithm) based optimization for HMM. Harmony search algorithm is sufficiently adaptable to allow incorporation of other techniques. A HMM training strategy using HSA is proposed, and it is tested on finding optimized structure for the pattern recognition of EEG sequence. The proposed HSA-HMM can performs global searching without initial parameter setting, local optima, and solution divergence.

• 정보과학회 논문지
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• 제41권12호
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• pp.1050-1057
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• 2014

뇌전도 기반의 뇌-컴퓨터 인터페이스는 향후 손 또는 발과 같은 신체를 대체하거나 사용자의 편의성을 제고하는 등의 다양한 목적으로 여러 산업에서 사용이 될 수 있는 기술이다. 본 논문에서는 경험 모드 분해와 고속푸리에 변환을 통해 동작 상상 뇌전도 신호를 분해하고 특징을 추출하는 방법을 제안한다. 뇌전도 신호 분류 과정은 다음과 같이 3단계로 구성된다. 신호 분해에서는 경험모드분해를 이용하여 뇌전도 신호에 대한 내재모드함수를 생성한다. 특징 추출에서는 파워 스펙트럼 밀도를 이용하여 생성된 내재모드함수의 주파수 대역을 확인한 뒤, 뮤파 대역을 포함하고 있는 내재모드함수에 고속푸리에 변환을 적용하여 움직임 상상에 대한 특징을 추출한다. 특징 분류에서는 서포트 벡터 머신을 사용하여 동작 상상 뇌전도 신호에 대한 특징을 분류하고, 10-교차검증을 통해 분류기의 일반화 성능을 추정한다. 제안하는 방법은 다른 방법들과 비교하여 84.50%의 분류 정확도를 보여주었다.

• 센서학회지
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• 제25권4호
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• pp.257-263
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• 2016

Among several technologies related to human brain, Brain Computer Interface (BCI) system is one of the most notable technologies recently. Conventional BCI for direct communication between human brain and machine are discomfort because normally electroencephalograghy(EEG) signal is measured by using multichannel EEG sensor. In this study, we propose 2-channel EEG sensor-based application control system which is more convenience and low complexity to wear to get EEG signal. EEG sensor module and system algorithm used in this study are developed and designed and one of the BCI methods, Motor Imagery (MI) is implemented in the system. Experiments are consisted of accuracy measurement of MI classification and driving control test. The results show that our simple wearable system has comparable performance with studies using multi-channel EEG sensor-based system, even better performance than other studies.

• IEIE Transactions on Smart Processing and Computing
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• 제4권2호
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• pp.97-102
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• 2015

In this paper, an extension of the standard common spatial pattern (CSP) algorithm using the strong uncorrelated transform (SUT) is used in order to extract the features for an accurate classification of the left- and right-hand motor imagery tasks. The algorithm is designed to analyze the complex data, which can preserve the additional information of the relationship between the two electroencephalogram (EEG) data from distant channels. This is based on the fact that distant regions of the brain are spatially distributed spatially and related, as in a network. The real-world left- and right-hand motor imagery EEG data was acquired through the Physionet database and the support vector machine (SVM) was used as a classifier to test the proposed method. The results showed that extracting the features of the pair-wise channel data using the strong uncorrelated transform complex common spatial pattern (SUTCCSP) provides a higher classification rate compared to the standard CSP algorithm.

• 디지털융복합연구
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• 제11권8호
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• pp.235-242
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• 2013

본 연구는 아급성뇌졸중환자의 상지기능과 일상생활수행능력에 대한 상상훈련의 효과를 알아보기 위해 시행되었다. 연구대상자들은 총 16 명으로 상상훈련군과 대조군에 8 명씩 무작위로 할당되었다. 상상훈련군은 4주 동안 주 5회, 매일 30의 상상훈련과 30분의 과제지향훈련을 시행하였고 대조군은 4주 동안 주 5회, 매일 30분간 과제지향훈련을 시행하였다. 측정은 상지기능의 변화를 알아보기 위해 울프운동 기능검사(Wolf motor function test, WMFT)와 Fugl-meyer 운동기능평가(Fugl-Meyer motor function assessment, FMA)를 측정하였고, 일상생활수행능력의 변화를 알아보기 위해 수정된 바델 지수(modified Barthel index, MBI)를 사용하여 측정하였다. 본 연구의 결과는 상상훈련이 대조군에 비해 모든 검사에서 더 유의한 향상을 보였다(p<.05). 그리고 훈련 전후의 효과크기는 상상훈련을 시행하였을 때 WMFT와 FMA는 각각 1.59, 2.02로 큰 효과를 나타냈으며, MBI는 0.37로 최소의 효과를 나타났다. 이러한 결과는 상상훈련이 상지기능과 일상생활수행능력 향상에 도움이 될 수 있으며, 상상훈련의 임상적용 가능성을 지지한다.