The Analysis of EEG for Yes-No and Multiple-Choice Questions
| Autor: | 李超然 |
|---|---|
| Rok vydání: | 2008 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 96 In the field of educational psychology and cognitive neuroscience, the analysis on the activities of cerebrum cognition is generally researched at present. This study is not only to analyze and classify the electroencephalography signals in favor of being applied on the Brain Computer Interface, or BCI, but also to discuss the effect on the cognitive thinking which is due to being inducted by different kinds of questions. The analysis of this study is based on the frequently used EEG bands in recent years. By means of the multiple-choice questions, we use intelligence test, this study discusses the differences on the energy of these bands for being tested by different kinds of questions. In contrast with the energy of Alpha band, the study results show that the energy of Theta band as the testee doing the math questions is much higher than that of Theta band as the testee doing the geometry questions. The energy of the Gamma band shows no differences on cognitive activities for being tested by different kinds of questions. At the identification portion, this experiment is to find out the method of characteristic acquisition, to identify the electroencephalography of the testee as he imaging answering for ‘yes’ or ‘no’, and to find out the least and the most suitable channel in order to minimize the quantity of operation. Algorithms of feature detection and classification are the two keys to EEG classifying. In the past, most articles focused on the improvement of classifiers, but selecting simper and more important feature is an alternative way to get a high accuracy. The feature extraction can be obtained by the Linear Discriminant Analysis (LDA). The method also uses Nearest Neighbor Rule (NNR) to classify the processed data.The experimental results show that the average accuracy rate is improved to 99% by C3、C4 and F3 channels. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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