Paper Info
Title
A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.
Abstract
Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much easier to acquire and process. To tackle this problem, we propose a cross-modality deep learning method based on deep canoncial correlation analysis (CDCCA) to transform each modality separately and coordinate different modalities into a hyperspace by using specific canonical correlation analysis constraints. In our proposed method, only eye movement signals are used as inputs in the test phase and the knowledge from EEG signals is learned in the training stage. Experimental results on two human decision confidence datasets demonstrate that our proposed method achieves advanced performance compared with the existing single-modal approaches trained and tested on eye movement signals and maintains a competitive accuracy in comparison with multimodal models.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871605
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DocType
Volume
ISSN
Conference
2022
2694-0604
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Cheng Fei100.34
Rui Li21916.31
Li-Ming Zhao301.35
Ziyi Li402.03
Bao-Liang Lu52361182.91