• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.263-270
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• 2004

Recently, wavelet transform have been applied to various kinds of problems in many fields. In this paper, we propose method of Daubechies wavelet to detect several kinds of important characteristic waves in tasks EEG that are needed to diagnose EEG. We show that our system could be attained higher performance in detecting characteristic waves than the other methods. In this system, the architecture of the neural network is a three layered feed-forward networks with one hidden layer which implements the error back propagation teaming algorithm. Applying the algorithms to 4 subjects show 92% classification rates. The proposed system shows a little more accurate diagnosis for task EEG by Wavelet and neural network. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and quantitative diagnosis of task EEG.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

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

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2875-2878
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• 2003

In this paper, we propose an EEG-based mental state prediction method during a mental tasks. In the experimental task, a subject goes through the process of responding to visual stimulus, understanding the given problem, controlling hand motions, and hitting a key. Considering the subject's varying brain activities, we model subjects' mental states with defining selection time. EEG signals from four subjects were recorded while they performed three mental tasks. Feature vectors defined by these representations were classified with a standard, feed-forward neural network trained via the error back-propagation algorithm. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or cognitive decision discrimination methods.

• Journal of the Korean Society of Safety
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• v.23 no.3
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• pp.65-70
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• 2008

Electroencephalogram(EEG) would be the most objective psychophysiological research technique on human errors though few research has been taken yet. This study aimed to get characteristics of human error while committing simple Odd-Ball tasks by utilizing the power spectrum technique of EEG data. Each experiment was composed of 3 tasks with different rules, and three young undergraduate students participated in this study as paid subjects. The result showed that subject and the interaction of subject and task factors were statistically significant on variation of power of $\alpha$ and $\beta$ bands which implied there would exist groups with homogeneity in their response. And though the variation of band powers due to task factors were not so great as to get statistical significance, it implied that the task requiring decoding process would be more strange to human beings than the task merely requiring psychological recall process.

• The Mathematical Education
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• v.55 no.3
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• pp.383-396
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• 2016

This study investigated highschool students' brain waves on functional tasks such as a transition(F task) from equation to graph and the other transition(G task) vice versa. A total of 39 students participated in the study who attended a high school located in Gyunggi province. These students were divided into two groups, HMA and LMA by MASS test revised by Ko, & Yi (2012). The functional tasks for the stroop task to measure EEG were provided from a previous study, Seok(2015). The results indicated two groups on G tasks showed deeper and wider brain waves which demonstrated G tasks were more difficult than F tasks. However, HMA group had an effect of the non-psychological program which had given more chances on G tasks rather than F tasks within Students' Zone of Proximal Development. Also, HMA group's brain waves had more ranges in amplitude and width of waves. These results imply that the characteristics of students' brain waves with math anxiety are consistent to the previous studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1277-1282
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• 2014

In this paper, we aimed to analysis EEG signals related to concentration of adolescents using letter visual stimulus to induce the concentration. The visual stimulus tasks were searching errors of propositional particle in several sentences. In the EEG signals, we specially focussed on SMR waves and mid-beta waves according to the results of a preceding research. Therefore we presented position of channel and frequency band of mid-beta significantly related to the concentration waves as the experimental results.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.24-32
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• 2000

BCI(Brain-Computer Interface) is studied to control the machines with brain. In this study, an EEG(Electroencephalography) signal classification model is proposed. The model gets EEG pattern from each subject's brain and extracts characteristic features. The model discriminates the EEG patterns by using those extracted characteristic features of each subject. The proposed method classifies each pair of the given tasks and combines the results to give the final result. Four tasks such as rest, movement, mental-arithmetic calculation and point-fixing were used in the experiment. Over 90% of the trials, the model yielded successful results. The model exploits characteristic features of the subjects and the weight table that was produced after training. The analysis results of the model such as its high success rates and short processing time show that it can be used in a real-time brain-computer interface system.

• PNF and Movement
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• v.12 no.2
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• pp.97-106
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• 2014

Purpose: The purpose of this study was to investigate changes in EEG through attention. Concentration training and performing tasks are important factors in the improvement of motor learning ability. Methods: In the experiment, 22 healthy people were divided into two groups: the trail making test (TMT) group and the computerized neurocognitive function test (CNT) group. A one-way Neuro Harmony M test to see whether there was a significant difference among the groups. Results: The TMT group showed a significant increase in ${\alpha}$ wave, ${\alpha}$ wave sequence, and ${\beta}$ wave sequence; however, there were no significant differences in SMR wave, SMR wave sequence, and ${\beta}$ wave. The CNT group showed increases in ${\alpha}$ wave, ${\alpha}$ wave sequence, SMR wave, SMR wave sequence, and ${\beta}$ wave sequence; however, there was no significant difference in ${\beta}$ wave. In EEGs before and after two performance tasks were changed, there were significant differences in ${\beta}$ wave, SMR wave, SMR wave sequence; however, there were no significant differences in ${\alpha}$ wave sequence, ${\beta}$ wave, and ${\beta}$ wave sequence. Conclusion: Attention training and concentration training offer feedback and repetition for constant stimulus and response. Moreover, attention training and concentration training can contribute to new studies and motivation by developing fast sensory and motor skills through acceptable visual and auditory stimulation.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.361-381
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• 2016

This study proposed two topical relevance models, simple and complex models, using EEG/ERP techniques. In the simple model regarding simple search tasks, N300 and P3b components are used. The N300 is specific to the semantic processing of pictures and the P3b reflects mechanisms involved in the decision about whether an external stimulus matches or does not match an internal representation of a specific category. In the complex model regarding complex search tasks, on the other hand, N400 and P600 components are used. The N400 reflects activation of an amodel system that integrates both image-based and conceptual representations into a context, whereas the P600 is related to complex cognitive processes. Our research results can be used as a source to design an EEG-based interactive multimedia system.