應用腦波訊號及眼動追蹤分析評估消費者於電子商店之購買意圖

Applying EEG and Eye Tracking Analysis to Assess Consumers’ Purchase Intentions in e-Store

王佩馨(Wang Pei-Hsin)

指導教授 ： 陳灯能

國立屏東科技大學/管理學院/資訊管理系所/碩士(2019年)

https://doi.org/10.6346/NPUST201900247

摘要

本研究將以使用者的腦波訊號結合眼動追蹤行為建立眼動腦波偏好關聯模型，透過儀器所擷取到的專注度、冥想度、凝視時間以及注視位置等特徵值輸入至類神經網路中，使用倒傳遞類神經網路分類並建立眼動腦波偏好關聯模型，進而以此模型開發出一套以結合腦波訊號與眼動追蹤行為之使用者在觀看手機資訊平台時偏好的系統，並以實驗法驗證本研究的模型效能。研究結果將驗證腦波訊號結合眼動追蹤行為的正確率為79%，經實驗顯示近七成狀況高於單獨使用腦波或者單獨使用眼動的正確率，可應用於未來決策相關技術上。

關鍵字

腦波訊號 ； 眼動追蹤 ； 電子商務

並列摘要

This study is to design a brainwave-eye tracking preference correlations model by utilizing user’s brainwave information and eye-tracking behaviors. We collect users’ brainwave and eye-tracking data by utilizing electroencephalography (EEG) and eye tracking devices. After analyzing the collected data, we extract several features such as concentrating, wandering mind, or gazing time or gazing positions into a back-propagation neural networks (BPNN) model to portrait the user’s brainwave-eye tracking preference correlations based on brainwave signals and eye tracking behaviors, thereby designing and developing the smart phone information recommender system. The experimental results show that the recommender system combined with the brainwave analysis and eye tracking can achieve 79% accuracy, significantly higher than only using brainwave or eye tracking information. This research has highlighted a future direction for detecting preference research and development on brainwave and eye-tracking design.

並列關鍵字

brainwave ； eye-tracking ； e-Store

參考文獻

1. Aurup, G. M. M. (2011). User preference extraction from bio-signals: An experimental study. Concordia University,

2. Babcock, J. S., Pelz, J. B., & Fairchild, M. D. (2003). Eye tracking observers during rank order, paired comparison, and graphical rating tasks. Paper presented at the IS AND TS PICS CONFERENCE.

3. Buscher, G., Cutrell, E., & Morris, M. R. (2009). What do you see when you're surfing?: using eye tracking to predict salient regions of web pages. Paper presented at the Proceedings of the SIGCHI conference on human factors in computing systems.

4. Chang, C.-Y., Lo, C.-Y., Wang, C.-J., & Chung, P.-C. (2010). A music recommendation system with consideration of personal emotion. Paper presented at the Computer Symposium (ICS), 2010 International.

5. Dimigen, O., Sommer, W., Hohlfeld, A., Jacobs, A. M., & Kliegl, R. (2011). Coregistration of eye movements and EEG in natural reading: analyses and review. Journal of Experimental Psychology: General, 140(4), 552.