The Effect of Creativity test on the Galvanic Skin Response Signal and Detection with Support Vector Machine
سال انتشار: 1394
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 665
فایل این مقاله در 11 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
RSTCONF01_110
تاریخ نمایه سازی: 30 آبان 1394
چکیده مقاله:
The study of creativity and its effects on human life are growing and expanding. However, few studies have investigated the relationship between creativity and physiological changes. As galvanic skin response (GSR) analysis is often used to detect cognitive behavior, this paper presents a novel approach for distinction between creativity/normal states and high/low creative from GSR signals. To these ends, 21 linear and 3 nonlinear features of this signal were extracted. Torrance Tests of Creative Thinking (TTCT- Figural B), which consists of three tasks, was used. The GSR signals of 21 participants (12 men, 12 women and 21-12 years) were recorded. Applying Wilcoxon statistical test, significant differences between rest and each task of creativity were observed; however, better discrimination was performed between rest and the first task. To classify the extracted features, Support Vector Machine (SVM) was used. The results indicated that SVM in combination with linear features classification algorithm effectively reduced the complexity of the calculations then will be reach the maximum accuracy of 12.11% in separating rest from the second task. Also, maximum accuracy of 12..69 was observed in task 3 between high/low creative subjects. It has also shown that differentiation between the rest and creative states is better performed by linear analysis than that of nonlinear ones. In conclusion, the combination of the SVM classifier with linear and nonlinear features can be useful to show the relation between creativity and physiological changes.
کلیدواژه ها:
نویسندگان
Sahar Zakeri
M.Sc. Student, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Ataollah Abbasi
Assistant professor, Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
Ateke Goshvarpour
Ph. D. Student, Computational, Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
مراجع و منابع این مقاله:
لیست زیر مراجع و منابع استفاده شده در این مقاله را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود مقاله لینک شده اند :