The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the ru...The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.展开更多
In this study, by using the response speed and the number of errors resulting from the children’s concentration test through the fuzzy inference system and comparing it to the theta which is one of the EEG’s paramet...In this study, by using the response speed and the number of errors resulting from the children’s concentration test through the fuzzy inference system and comparing it to the theta which is one of the EEG’s parameter to find the level of concentration. Targeting 21(Male 12, Female 9) healthy children between the ages of 10 - 14, the test was conducted one time with a duration of 14 minutes. For the first 5 minutes the children were listening to the Bach’s Air on a G string having a steady state and the next 9 minutes the children were subjected to the external stimuli audiogenic stimulation that induces attention concentration. When the number 3 was heard, children were subjected to press down on the spacebar to check the response speed and the number of errors. By conducting computerized neurocognitive function test to compare the theta wave related to the concentration with the response speed and the number of errors that determines the attention concentration through the fuzzy system, the data from 15 children out of 21 have shown the results for the concentration. In order to check the concentration level, a fuzzy inference system which was designed by the user could be used.展开更多
文摘The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.
文摘In this study, by using the response speed and the number of errors resulting from the children’s concentration test through the fuzzy inference system and comparing it to the theta which is one of the EEG’s parameter to find the level of concentration. Targeting 21(Male 12, Female 9) healthy children between the ages of 10 - 14, the test was conducted one time with a duration of 14 minutes. For the first 5 minutes the children were listening to the Bach’s Air on a G string having a steady state and the next 9 minutes the children were subjected to the external stimuli audiogenic stimulation that induces attention concentration. When the number 3 was heard, children were subjected to press down on the spacebar to check the response speed and the number of errors. By conducting computerized neurocognitive function test to compare the theta wave related to the concentration with the response speed and the number of errors that determines the attention concentration through the fuzzy system, the data from 15 children out of 21 have shown the results for the concentration. In order to check the concentration level, a fuzzy inference system which was designed by the user could be used.
