Abstract
The attention of subjects to EEG-based emotion recognition experiments could seriously affect their emotion induction level and annotation quality of EEG data. Therefore, it is important to evaluate the raw EEG data before training the classification model. In this paper, we propose a framework to filter out low quality EEG data from participants with low attention using eye tracking data and boost the performance of deep affective models with CNN and LSTM. We introduce a novel attention-deprived experiment with dual tasks, in which the dominant task is auditory continuous performance test, identical pairs version (CPT-IP) and the subtask is emotion eliciting experiment. Motivated by the idea that subjects with attention share similar scan-path patterns under the same clips, we adopt the cosine distance based spatial-temporal scan-path analysis with eye tracking data to cluster these similar scan-paths. The average accuracy of emotion recognition using the selected EEG data with attention is about 3% higher than that of original training dataset without filtering. We also found that with the increasing distance of scan-paths between outliers and cluster center, the performance of corresponding EEG data tends to decrease.
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Acknowledgments
This work was supported in part by the grants from the National Key Research and Development Program of China (Grant No. 2017YFB1002501), the National Natural Science Foundation of China (Grant No. 61673266), and the Fundamental Research Funds for the Central Universities.
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Zhao, LM., Li, XW., Zheng, WL., Lu, BL. (2018). Active Feedback Framework with Scan-Path Clustering for Deep Affective Models. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_29
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DOI: https://doi.org/10.1007/978-3-030-04179-3_29
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