• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.223-230
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• 2005

This paper presents a new approach to investigate spatial correlation between independent components of brain alpha activity in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). To avoid potential problems of simultaneous fMRI and EEG acquisitions in imaging pure alpha activity, data from each modality were acquired separately under a 'three conditions' setup where one of the conditions involved closing eyes and relaxing, thus making it conducive to generation of alpha activity. The other two conditions -- eyes open in a lighted room or engaged in a mental arithmetic task, were designed to attenuate alpha activity. Using a Mixture Density Independent Component Analysis (MD-ICA) that incorporates flexible non-linearity functions into the conventional ICA framework, we could identify the spatiotemporal components of fMRI activations and EEG activities associated with the alpha rhythm. Then, the sources of the individual EEG alpha activity component were localized by a Maximum Entropy (ME) method that is specially designed to find the most probable dipole distribution minimizing the localization error in sense of LMSE. The resulting active dipoles were spatially transformed to 3D MRls of the subject and compared to fMRI alpha activity maps. A good spatial correlation was found in the spatial distribution of alpha sources derived independently from fMRI and EEG, suggesting the proposed method can localize the cortical areas responsible for generating alpha activity successfully in either fMRI or EEG. Finally a functional connectivity analysis was applied to show that alpha activity sources of both modalities were also functionally connected to each other, implying that they are involved in performing a common function: 'the generation of alpha rhythms'.

• Science of Emotion and Sensibility
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• v.19 no.1
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• pp.31-38
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• 2016

The brain and muscles both of which are composed of top-down structure occur the connectivity with the change of Electroencephalogram(EEG) and Electromyogram(EMG). In this paper, we studied the difference of functional connectivity between brain and muscles that by applying coherence method to EEG and EMG signals when users exercised upper limb with and without the movement intention. The changes in the EEG and EMG signals were inspected using coherence method. During the upper limb exercise, the mu (8~14 Hz) and beta (15~30 Hz) rhythms of the EEG signal at the motor cortex area are activated. And then the beta and piper (30~60 Hz) rhythms of the EMG signal are activated as well. The result of coherence analysis between EEG and EMG showed the coefficient of active exercise including movement intention is significantly higher than passive exercise. The coherence relations between cognitive response and muscle movement could interpret that the connectivity between the brain and muscle appear during active exercise with movement intention. The feature of coherence between brain and muscles by the status of movement intention will be useful in designing the rehabilitation system requiring feedback depending on the users' movement intention status.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.384-391
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• 2006

Variation of brain dynamics under Zen meditation has been one of our major research interests for years. One issue encountered is the inaccessibility to the actual meditation level or stage as a reference. In this paper, we propose an alternative strategy for investigating the human brain in response to external flash stimuli during Zen meditation course. To secure a consistent condition of the brain dynamics when applying stimulation, we designed a recording of flash visual evoked potentials (F-VEPs) based on a constant background EEG (electroencephalograph) frontal $\alpha-rhythm$ dominating activities that increase significantly during Zen meditation. Thus the flash-light stimulus was to be applied upon emergence of the frontal $\alpha-rhythm$. The alpha-dependent F-VEPs were then employed to inspect the effect of Zen meditation on brain dynamics. Based on the experimental protocol proposed, considerable differences between experimental and control groups were obtained. Our results showed that amplitudes of P1-N2 and N2-P2 on Cz and Fz increased significantly during meditation, contrary to the F-VEPs of control group at rest. We thus suggest that Zen meditation results in acute response on primary visual cortex and the associated parts.

• PNF and Movement
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• v.13 no.1
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• pp.17-23
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• 2015

Purpose: This study aims to identify changes in the mirror neuron system in normal people through mu rhythm during action observation of occupation-based intervention, purposeful activity and prepare a method of intervention form of occupation using occupation and activity. Methods: TThis study aims to identify changes in the mirror neuron system in normal people through mu rhythm during action observation of occupation-based intervention, purposeful activity and prepare a method of intervention form of occupation using occupation and activity. The activation of the mirror neuron system was compaired among 3 condition by oneway ANOVA. Results: The result of analysis showed mu suppression in all conditions. Although all conditions showed mu suppression, there was no significant difference among the conditions. Conclusion: The results suggest that the mirror neuron system is activated during action observation to be able to occupational therapy but the mirror neuron system is not separately activated among the conditions.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.461-470
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• 2007

Human can learn how to shape their brain electrical activity in a desired direction through continuous feedback of the electroencephalogram (EEG), and this technique is known as Neurofeedback (or EEG biofeedback), which has been used since the late 1960s in clinical applications. In this study, a portable wireless EEG (named wEEG) has been designed and implemented, which consists of a mobile station (a wireless two-channel EEG acquisition device) and a base station (a bridge between mobile station and computer). Moreover, a SensoriMotor Rhythm (SMR) training system was also implemented with the wEEG for enhancing attention with virtual environment. Experiment results based on 16 volunteers' (8 females and 8 males, average age is $27{\pm}4$) were reported in this paper. The results show that the SMR ratio of 87.5% subjects increased about 0.7% in training status than that in the stable status. With the proposed system, many training protocol scan be designed easily and can be done at home in our daily life conveniently. Additionally, the proposed system will be useful for disabled and aged people.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.967-971
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• 1989

A new system for automatic scoring of 'organization' of the EEG dominant rhythm was constructed and applied to 18 normal subjects and 15 patients. Organization parameters which best represented the 'organization' as judged by 5 neurologists' visual inspection were calculated and the automatic organization scoring was obtained by a linear regression of those organization parameters. Furthermore, values of the regression coefficients were used to study the characteristics of EEG interpretation by each neurologist, and this scoring technique can also be applied to the training of EEG interpretation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.3760-3768
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• 2015

This study was to examine the correlation of Personality and EEG. Personality test and EEG of the 40 team leader of a business enterprise were carried out at the same time and the correlation of test results were analyzed. Personality test was done by Big 5 and brain waves were measured by 2-Channel EEG System at Fp1 and Fp2. The analysis showed a positive correlation between the Big 5 agreeableness and SRQ(Self Regulation Quotient) relaxation status which is related with alpha rhythm, and showed a negative correlation between the Big 5 openness and SRQ concentration status which is related with low_beta rhythm. It means that the personality is closely correlated with human brain cortex activation and can be checked by brainwave analysis.

• Clinical and Experimental Pediatrics
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• v.54 no.1
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• pp.22-28
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• 2011

Purpose: To investigate the clinical characteristics of late-onset epilepsy combined with autism spectrum disorder (ASD), and the relationship between certain types of electroencephalography (EEG) abnormalities in ASD and associated neuropsychological problems. Methods: Thirty patients diagnosed with ASD in early childhood and later developed clinical seizures were reviewed retrospectively. First, the clinical characteristics, language and behavioral regression, and EEG findings of these late-onset epilepsy patients with ASD were investigated. The patients were then classified into 2 groups according to the severity of the EEG abnormalities in the background rhythm and paroxysmal discharges. In the severe group, EEG showed persistent asymmetry, slow and disorganized background rhythms, and continuous sharp and slow waves during slow sleep (CSWS). Results: Between the two groups, there was no statistically significant difference in mean age (P=0.259), age of epilepsy diagnosis (P=0.237), associated family history (P=0.074), and positive abnormal magnetic resonance image (MRI) findings (P=0.084). The severe EEG group tended to have more neuropsychological problems (P=0.074). The severe group statistically showed more electrographic seizures in EEG (P=0.000). Rett syndrome was correlated with more severe EEG abnormalities (P=0.002). Although formal cognitive function tests were not performed, the parents reported an improvement in neuropsychological function on the follow up checkup according to a parent's questionnaire. Conclusion: Although some ASD patients with late-onset epilepsy showed severe EEG abnormalities, including CSWS, they generally showed an improvement in EEG and clinical symptoms in the longterm follow up. In addition, severe EEG abnormalities tended to be related to the neuropsychological function.

• Journal of Biomedical Engineering Research
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• v.29 no.1
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• pp.17-24
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• 2008

The aim of this paper is to report our preliminary results of investigating the spatial focalization of Zen-meditation EEG (electroencephalograph) in alpha band (8-13 Hz). For comparison, the study involved two groups of subjects, practitioners (experimental group) and non-practitioners (control group). To extract EEG alpha rhythm, wavelet analysis was applied to multi-channel EEG signals. Normalized alpha-power vectors were then constructed from spatial distribution of alpha powers, that were classified by Fuzzy C-means based algorithm to explore various brain spatial characteristics during meditation (or, at rest). Optimal number of clusters was determined by correlation coefficients of the membership-value vectors of each cluster center. Our results show that, in the experimental group, the incidence of frontal alpha activity varied in accordance with the meditation stage. The results demonstrated three different spatiotemporal modules consisting with three distinctive meditation stages normally recognized by meditation practitioners. The frontal alpha activity in two groups decreased in different ways. Particularly, monotonic decline was observed in the control group, and the experimental group showed increasing results. The phenomenon might imply various mechanisms employed by meditation and relaxation in modulating parietal alpha.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.