| Summary: | 碩士 === 國立清華大學 === 服務科學研究所 === 102 === As the service design becomes more important and highly related to customers’ needs, there are several methodologies could be used for user research. However, these existed methodologies may not be able to fit to all situations; for example, researchers cannot directly observe and record users’ mental states. In this research, we adopt a commercial EEG-based device with single electrode in the setting of listening to English to record their mental states. This research aims to develop a classifier that can predict users’ mental efforts via experiments in which 35 college students were asked to listen to English and respond their mental states, easy or difficult, before choosing the answer for each question. We chose Support Vector Machine (SVM) as the classifier, and valuated its performance in terms of recall, precision, and F-measure in five different feature models with different time windows of extracting EEG data. The results indicate that one of the proposed features gives the highest F-measure of 0.353 with precision rate of 65.8% and recall rate of 57.1% from EEG data extracted in the time window of five seconds. Besides, in some situations, the more features we include for classification, the lower F-measure scores the system obtains. This research has contributed to the literature that a classification of mental effort using SVM classifier is effective to capture users’ mental states in the process of listening English and comprehension. The embedded SVM classifier could be used for detecting users’ mental state in service design process in the real world application on language learning activities. Additional efforts can be made to extend its applications on different service contexts.
|
|---|
