• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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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.

• PNF and Movement
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• v.7 no.4
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• pp.45-53
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• 2009

Purpose : The purpose of this article was to review the literature on mirror neuron system with reference to its functional diversity in stroke rehabilitation.. Method : This review outlines scientific findings regarding different neurophysiological properties in mirror neurons, and discusses their involvement in process of stroke rehabilitation. Result & Conclusions : Mirror neurons were first discovered in macaque monkey. These neurons, like most neurons in F5 areas in premotor cortex, fired when an individual performs an action, as well as when he/she observes a similar action done by another individual, although originally fired only during action execution. Mirror neurons form a network for motor planning and initiating of motor action. Thus, in stroke rehabilitation based on the mirror neuron-action observation, motor imagery, observation with intent to imitate and imitation-may help activate mirror neuron system for improved outcome of physical therapy. These studies provide a scientific theoretical basis and discuss for the use of mirror neuron system as a complement to clinical physical therapy in stroke rehabilitation.

• Journal of KIISE
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• v.44 no.6
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• pp.587-594
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• 2017

Recently, motor imagery electroencephalogram(EEG) based Brain-Computer Interface(BCI) systems have received a significant amount of attention in various fields, including medicine and engineering. The Common Spatial Pattern(CSP) algorithm is the most commonly-used method to extract the features from motor imagery EEG. However, the CSP algorithm has limited applicability in Small-Sample Setting(SSS) situations because these situations rely on a covariance matrix. In addition, large differences in performance depend on the frequency bands that are being used. To address these problems, 4-40Hz band EEG signals are divided using nine filter-banks and Regularized CSP(R-CSP) is applied to individual frequency bands. Then, the Mutual Information-Based Individual Feature(MIBIF) algorithm is applied to the features of R-CSP for selecting discriminative features. Thereafter, selected features are used as inputs of the classifier Least Square Support Vector Machine(LS-SVM). The proposed method yielded a classification accuracy of 87.5%, 100%, 63.78%, 82.14%, and 86.11% in five subjects("aa", "al", "av", "aw", and "ay", respectively) for BCI competition III dataset IVa by using 18 channels in the vicinity of the motor area of the cerebral cortex. The proposed method improved the mean classification accuracy by 16.21%, 10.77% and 3.32% compared to the CSP, R-CSP and FBCSP, respectively The proposed method shows a particularly excellent performance in the SSS situation.

• Journal of the Korean Society of Physical Medicine
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• v.5 no.3
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• pp.395-404
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• 2010

Purpose : The object of this study was to examine the effect of motor learning on brain activation depending on the method of motor learning. Methods : The brain activation was measured in 9 men by fMRI. The subjects were divided into the following groups depending on the method of motor learning: actually practice (AP, n=3) group, action observation (AO, n=3) group and motor imagery (MI, n=3) group. In order to examine the effect of motor learning depending on the method of motor learning, the brain activation data were measured during learning. For the investigation of brain activation, fMRI was conducted. Results : The results of brain activation measured before and during learning were as follows; (1) During learning, the AP group showed the activation in the following areas: primary motor area located in precentral gyrus, somatosensory area located in postcentral gyrus, supplemental motor area and prefrontal association area located in precentral gyrus, middle frontal gyrus and superior frontal gyrus, speech area located in superior temporal gyrus and middle temporal gyrus, Broca's area located in inferior parietal lobe and somatosensory association area of precuneus; (2) During learning, the AD groups showed the activation in the following areas: primary motor area located in precentral gyrus, prefrontal association area located in middle frontal gyrus and superior frontal gyrus, speech area and supplemental motor area located in superior temporal gyrus and middle temporal gyrus, Broca's area located in inferior parietal lobe, somatosensory area and primary motor area located in precentral gyrus of right cerebrum and left cerebrum, and somatosensory association area located in precuneus; and (3) During learning, the MI group showed activation in the following areas: speech area located in superior temporal gyrus, supplemental area, and somatosensory association area located in precuneus. Conclusion : Given the results above, in this study, the action observation was suggested as an alternative to motor learning through actual practice in serial reaction time task of motor learning. It showed the similar results to the actual practice in brain activation which were obtained using activation of mirror neuron. This result suggests that the brain activation occurred by the activation of mirror neuron, which was observed during action observation. The mirror neurons are located in primary motor area, somatosensory area, premotor area, supplemental motor area and somatosensory association area. In sum, when we plan a training program through physiotherapy to increase the effect during reeducation of movement, the action observation as well as best resting is necessary in increasing the effect of motor learning with the patients who cannot be engaged in actual practice.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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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 Magnetics
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• v.19 no.2
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• pp.181-184
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• 2014

The aim of the present study was to examine whether motor imagery (MI) practice in conjunction with repetitive transcranial magnetic stimulation (rTMS) applied to stroke patients could improve theirgait ability. This study was conducted with 29 subjects diagnosed with hemiparesis due to stroke.The experimental group consisted of 15 members who were performed MI practice in conjunction with repetitive transcranial magnetic stimulation, while the control group consisted of 14 members who were performed MI practice and sham therapy. Both groups received traditional physical therapy for 30 minutes a day, 5 days a week, for 6 weeks; additionally, they received mental practice for 15 minutes. The experimental group was instructed to perform rTMS and the control group was instructed to apply sham stimulation for 15 minutes. Gait analysis was performed using a three-dimensional motion capture system, which is a real-time tracking device that delivers data via infrared reflective markers using six cameras. Results showed that the velocity, step length, and cadence of both groups were significantly improved after the practice (p<0.05). Significant differences were found between the groups in velocity and cadence (p<0.05) as well as with respect to the change rate (p<0.05) after practice. The results showed that MI practice in conjunction with rTMS is more effective in improving gait ability than MI practice alone.

• The Korean Journal of Pain
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• v.35 no.2
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• pp.152-159
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• 2022

Background: Subjects with frozen shoulder (FS) might not be comfortable with vigorous physical therapy. Clinical trials assessing the effect of graded motor imagery (GMI) in FS are lacking. The aim of this study was to determine the effect of GMI as an adjunct to conventional physiotherapy in individuals with painful FS. Methods: Twenty subjects aged 40-65 years having stage I and II of FS were randomly divided into two study groups. The conventional physiotherapy group (n = 10) received electrotherapy and exercises while the GMI group (n = 10) received GMI along with the conventional physiotherapy thrice a week for 3 weeks. Pre- (Session 1) and post- (Session 9) intervention analysis for flexion, abduction, and external rotation range of motion (ROM) using a universal goniometer, fear of movement using the fear avoidance belief questionnaire (FABQ), pain with the visual analogue scale, and functional disability using the shoulder pain and disability index (SPADI) was done by a blinded assessor. Results: Statistically significant difference was seen within both the groups for all the outcomes. In terms of increasing abduction ROM as well as reducing fear of movement, pain, and functional disability, the GMI group was significantly better than control group. However, both groups were equally effective for improving flexion and external rotation ROM. Conclusions: Addition of GMI to the conventional physiotherapy proved to be superior to conventional physiotherapy alone in terms of reducing pain, kinesiophobia, and improving shoulder function for stage I and II of FS.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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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.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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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.