• PNF and Movement
• /
• v.22 no.1
• /
• pp.139-149
• /
• 2024

Purpose: This study aimed to investigate the effects of gradually increasing treadmill inclination on the electroencephalogram (EEG) of stroke patients during gait training. Methods: Three stroke patients who were diagnosed with stroke within six months and capable of walking on a treadmill were selected as subjects. EEG electrodes were attached at Fp1, Fp2, F3, F4, C3, C4, P3, and P4 positions of the cerebral hemispheres using the International 10-20 system. The intervention involved walking for 2 minutes each at 0 degrees, 15 degrees, and 30 degrees inclination on the treadmill while focusing on a target point located in front during the treadmill gait training. The EEG (Smartingmobi, Serbia) generated when the treadmill gradient gradually increased was measured. In addition, relative alpha and relative beta waves were visualized through the Brain mapping program in the TeleScan program to assess the changes in each brain region for the activity of the EEG. Results: The relative alpha wave value decreased as treadmill inclination increased, while the relative beta wave value increased. Conclusion: Gradually increasing the inclination during treadmill gait training appears to be a crucial parameter for increasing the brain activity levels of stroke patients.

• Proceedings of the IEEK Conference
• /
• 2009.05a
• /
• pp.145-147
• /
• 2009

This research measures EEG signals which are generating on head skin and extracts brain concentration level related with brain activity. We have developed concentration wireless transmission system by displaying this EEG signal on PDA mobile device. The front head was used for measuring EEG signal and INA128 with TL084 and analog elements was used for measuring EEG signal, amplifying and filtering the signal. Measured analog EEG signals changed into digital signals by using ADC of PIC24FJ192 with 10bit resolution and 500Ks/s sampling rate. So The changed digital signals have transmitted to the PDA by using bluetooth. LabView 8.5 was also used for FFT transformation, frequency and spectrum analysis of the transferred EEG signal. As a result, $\alpha$ wave, $\beta$ wave, $\theta$ wave and $\delta$ wave were classified. we extracted the concentration index by adapting concentration extraction algorithm. This concentration index was transferred into PDA by wireless module and displaying.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.117-120
• /
• 1995

The electronencephalogram (EEG) is a complex electrical signal which reflects generalized brain activity. The EEG is utilized in the clinical assesment of many neurological and psychiatric disorders and offers promise for monitoring of patients undergoing operation. This paper describes a technique for quantitative analysis of EEG signals which is based on an eigenspace. Examples of the application approach to simulated and clinical EEG data illustrate the capabilities.

• Nuclear Engineering and Technology
• /
• v.51 no.3
• /
• pp.683-691
• /
• 2019

In this paper, we propose a novel method to quantify the neural synchronization between subjects in the collaborative process through electroencephalogram (EEG) hyperscanning. We hypothesized that the neural synchronization in EEGs will increase when the communication of the operators is smooth and the teamwork is better. We quantified the EEG signal for multiple subjects using a representative EEG quantification method, and studied the changes in brain activity occurring during collaboration. The proposed method quantifies neural synchronization between subjects through bispectral analysis. We found that phase synchronization between EEGs of multi subjects increased significantly during the periods of collaborative work. Traditional methods for a human error analysis used a retrospective analysis, and most of them were analyzed for an unspecified majority. However, the proposed method is able to perform the real-time monitoring of human error and can directly analyze and evaluate specific groups.

• Journal of Oriental Neuropsychiatry
• /
• v.18 no.1
• /
• pp.37-61
• /
• 2007

Objective : By examining EEG status in Korean Traditional Medicine (KTM) from the viewpoint of 'form-qi theory(形氣論)', We wish to prepare for the fundamentals of applicability of KTM diagnoses to EEG. In addition, through reinterpretation of existing Western Medicine reports from the viewpoint of KTM, We tried to find out interrelationship between them. Method : In this paper, a methodology applicable to KTM diagnoses of EEG is presented from the EEG features in waveform characteristics, personalized diversity, and cognitive activity reflection. Results : Frequency bands are assigned to corresponding one of the eight trigrams in terms of yin/yang balance, which is analogous with EEG spectrum analysis mostly used in EEG quantification. The amplitude ratio of each EEG for each frequency band gives meaningful index numbers which can be used in EEG data interpretation, and every index number is named after the sixty four hexagrams. These approaches are adopted through both '4-band classification system and '6-band classification system', and applied to pre-existing reported EEG data obtained from normal adults. These analyses show that changes and distribution pattern in the index numbers are observed as a whole on both left-right line and front-back line connecting EEG measurement cephalic electrodes. And differences in distribution pattern of three index numbers deduced from '6-band classification system' are discussed according to constitution. Conclusion : The index numbers introduced here, which are the spectral power ratio for each EEG, are based on KTM yin/yang balance. These index numbers vary according to cephalic location, so its application in terms of traditional meridian theory is strongly expected. The index number distribution also shows different patterns according to constitution.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.2
• /
• pp.367-372
• /
• 2023

In this paper, we analyzed through the EEG signals how musical stimulus affects learning activities. Musical stimuli were divided into sedative and stimulative tendency music, preferred and non-preferred music, and the learning activity tasks were divided into mathematics tasks and memorization tasks. The signals measured in the EEG experiments were analyzed with the power spectrum of SMR waves known to be related to human concentration. Those spectra used for quantitative comparison in this paper. As a result the power of the EEG signals was observed to be greater than the case where music was given as a stimulus. Regardless of the type of task, the power of the EEG signals was observed to be greater in the case of sedative tendency than in the case of stimulative tendency, and the power of the EEG signals was observed to be greater in the case of favorite music than in the case of unfavorite music. From these results, it is estimated that if the musical stimulus exists, in the case of sedative tendency music, and in the case of favorite music, concentration can be increased than in the relative case.

• Progress in Medical Physics
• /
• v.5 no.1
• /
• pp.41-53
• /
• 1994

The accurate localization of electrical sources in the brain is one of the most important questions in EEG, especially in the analysis of evoked responses and of epileptiform spike activity. A detailed simulation study of single dipole source estimation based on EEG is given in this paper. The effects of dipole model parameters on single dipole source tracing in EEG are examined in some detail using the Monte Carlo simulation. The error of source localization is found to be greatly influenced by how the electrodes are distributed over the head and the number of them.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.3
• /
• pp.1060-1071
• /
• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• Journal of Biomedical Engineering Research
• /
• v.9 no.1
• /
• pp.31-36
• /
• 1988

A personal computer-based brain map is described which will display a gray scale maps showing the distribution of signals derived from the electrical activity of the brain such as EEG or EP This topographic brain mapping system has a flexibility which describe the electrode number and placement mapping onto any shaped space and generate a brain maps by incoorporated the data acquisition and processing software with conventional EEG machine.

• Journal of Naturopathy
• /
• v.11 no.2
• /
• pp.79-84
• /
• 2022

Background: Exercise influences the generation of brain cells through learning and experience in the process of acquiring motor skills and helps improve brain function. It is necessary to scientifically verify how brain wave activity, a method of analyzing brain function, affects movement. Purposes: We scientifically identify the positive effects on EEG activity when applying complex functional linear ladder movements in an appropriate environment. Methods: After recruiting 30 male university students, we divided them into a linear ladder exercise group, a treadmill exercise group, and a control group, and exercise was applied and measured repeatedly for ten weeks. Results: There was a statistically significant change between groups in the left prefrontal lobe of alpha waves when exercise was applied (p < .05). Conclusions: Although exercise has a positive effect on EEG, line ladder exercise, which applies a complex pattern and produces more leg movement, appears to have a better impact on brain function than traditional aerobic exercise.