• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.4
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• pp.277-282
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• 2015

Braincomputer interface (BCI) technology is making advances in the field of humancomputer interaction (HCI). To improve the BCI technology, we study the changes in the electroencephalogram (EEG) signals for six levels of grip strength: 10%, 20%, 40%, 50%, 70%, and 80% of the maximum voluntary contraction (MVC). The measured EEG data are categorized into three classes: Weak, Medium, and Strong. Features are then extracted using power spectrum analysis and multiclass-common spatial pattern (multiclass-CSP). Feature datasets are classified using a support vector machine (SVM). The accuracy rate is higher for the Strong class than the other classes.

• Journal of Magnetics
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• v.16 no.3
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• pp.259-262
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• 2011

Compared to acupuncture, the pulsed magnetic field (PMF) stimulus is a useful tool for treatment of many physical conditions and health maintenance due to its advantages as a noninvasive and nontoxic medical treatment. The purpose of this study was to investigate the effect of PMF stimulus direction at PC9 on the alpha activity of electroencephalogram (EEG) and vascular aging calculated from photoplethysmograph (PPG). It can be concluded that the direction of PMF stimulus affects the increase of alpha activity of EEG and PPG, indicating the vascular stiffness and the sclerosis level of blood vessels weakly relevant to the direction of PMF stimulus.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1731-1733
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• 2015

Since electroencephalogram(EEG) has non-linear and non-stationary properties, it is effective to analyze the characteristic of EEG with time-frequency method rather than spectrum method. In this letter, we propose the modified drowsiness detection system using discrete wavelet transform combined with errors-in-variables and multilayer perceptron methods. For the comparison of the proposed scheme with the previous one, the state 'others' is added to the previous states of drivers: 'alertness,' 'transition,' and 'drowsiness.' From the computer simulation using machine learning, we confirm that the proposed scheme outperforms the previous one for some conditions.

• Sleep Medicine and Psychophysiology
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• v.15 no.1
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• pp.5-11
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• 2008

Sleep is an essential process maintaining the life cycle of the human. In parallel with physiological, cognitive, subjective, and behavioral changes that take place during the sleep, there are remarkable changes in the electroencephalogram (EEG) that reflect the underlying electro-physiological activity of the brain. However, analyzing EEG and relating the results to clinical observations is often very hard due to the complexity and a huge data amount. In this article, I introduce several linear and non-linear tools, developed to analyze a huge time series data in many scientific researches, and apply them to EEG to characterize various sleep states. In particular, the spectral analysis, detrended fluctuation analysis (DFA), and synchrony analysis are administered to EEG recorded during nocturnal polysomnography (NPSG) processes and daytime multiple sleep latency tests (MSLT). I report that 1) sleep stages could be differentiated by the spectral analysis and the DFA ; 2) the gradual transition from Wake to Sleep during the sleep onset could be illustrated by the spectral analysis and the DFA ; 3) electrophysiological properties of narcolepsy could be characterized by the DFA ; 4) hypnic jerks (sleep starts) could be quantified by the synchrony analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

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

• KIPS Transactions on Software and Data Engineering
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• v.5 no.4
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• pp.195-202
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• 2016

There have been many studies on subject tests for 3D contents using 3D glasses, but there is a limited research for 3D contents using autostereoscopic display. In this study, we investigated to assess usability of electroencephalogram (EEG) as an objective evaluation for 3D contents with different quality using autosteroscopic display, especially for lenticular lens type. The image with optimal quality and the image with distorted quality were separately generated for autostereosopic display with lenticular lens type and displayed sequentially through lenticular lens for 26 subjects. EEG signals of 8 channels from 26 subjects exposed to those images were detected and correlation between EEG signal and the quality of 3D images were statistically evaluated to check differences between optimal and distorted 3D contents. What we found was that there was no statistical significance for a wave vibration, however b wave vibration shows statistically significant between optimal and distorted 3D contents. b wave vibration observed for the distorted 3D image was stronger than that for the optimal 3D image. This results suggest that subjects viewing the distorted 3D contents through lenticular lens experience more discomfort or fatigue than those for the optimum 3D contents, which resulting in the greater b wave activity for those watching the distorted 3D contents. In conclusion, these results confirm that electroencephalogram (EEG) analysis can be used as a tool for objective evaluation of 3D contents using autosteroscopic display with lenticular lens type.

• The Korean Journal of Physiology and Pharmacology
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• v.28 no.3
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• pp.253-264
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• 2024

Chronic neuropathic pain (CNP) is a complex condition often arising from neural maladaptation after nerve injury. Understanding CNP complications involves the intricate interplay between brain-heart dynamics, assessed through quantitative electroencephalogram (qEEG) and heart rate variability (HRV). However, insights into their interaction in chronic pain are limited. Resting EEG and simultaneous electrocardiogram (lead II) of the participants were recorded for qEEG and HRV analysis. Correlations between HRV and qEEG parameters were calculated and compared with age, sex, and body mass index (BMI)-matched controls. CNP patients showed reduced HRV and significant increases in qEEG power spectral densities within delta, theta, and beta frequency ranges. A positive correlation was found between low frequency/high frequency (LF/HF) ratio in HRV analysis and theta, alpha, and beta frequency bands in qEEG among CNP patients. However, no significant correlation was observed between parasympathetic indices and theta, beta bands in qEEG within CNP group, unlike age, sex, and BMI-matched healthy controls. CNP patients display significant HRV reductions and distinctive qEEG patterns. While healthy controls exhibit significant correlations between parasympathetic HRV parameters and qEEG spectral densities, these relationships are diminished or absent in CNP individuals. LF/HF ratio, reflecting sympathovagal balance, correlates significantly with qEEG frequency bands (theta, alpha, beta), illuminating autonomic dysregulation in CNP. These findings emphasize the intricate brain-heart interplay in chronic pain, warranting further exploration.

• Korean Journal of Acupuncture
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• v.29 no.3
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• pp.467-475
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• 2012

Objectives : The aim of this study was to examine the effects of Acupuncture at the GV20 and GV22 on normal human beings using power spectrum analysis. Methods : Electroencephalogram(EEG) power spectrum exhibits site-specific and state-related differences in various frequency bands. 8 channels Background Electroencephalogram (EEG) was carried out in 30 subjects(24 females and 4 males). Results : In ${\delta}$(theta) band, the power values decreased significantly at the 8-channel average value(p=0.03) and especially at T3(p=0.02), T4(p=0.001) and P3(p=0.03). In ${\alpha}$(alpha) band, the power values have no significant changes. In ${\beta}$(beta)band, the power values increased significantly at the 8-channel average value (p=0.02) and especially at T4(p=0.003), P3 (p= 0.03) and P4(0.02). In ${\beta}/{\delta}$(beta/theta) ratio, the value increased significantly at the 8-channel average value(p=0.002) and especially at Fp2(p=0.05), F4(p=0.007), T3(0.012), T4(0.005), P3 (0.007) and P4(0.03) Conclusions : Through this data, we conclude that acupuncture at the GV20 and GV22 on normal human beings could have possibility to awake the cerebral cortex by the functional mechanism.