The Establishment and Application of a Micro-expression Dataset with EEG Signals
Pages 726 - 729
Abstract
The current success of micro-expression recognition methods based on computer vision techniques relies heavily on the available established video micro-expression datasets. The recognition of micro-expression using electroencephalography (EEG) signals is a question worth to explore, but the relevant datasets are not established. In this paper, we reviewed the previously developed micro-expression datasets and reported a new emotion expression inhibition paradigm with real-time supervision (EEIPS). Through synchronizing the high-speed camera and EEG acquisition, micro-expression video and EEG data of 68 undergraduates were collected; a dataset called SWUME of 806 micro-expression and 393 macro-expression samples was established. To verify the validity of this dataset, we proposed a method based on machine learning classification with ten-fold cross validation after feature extraction. Results indicated that the best classification accuracy between micro-expressions and macro-expressions can reach 90%. This suggests that micro-expressions can be effective recognized based on EEG data.
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Published In
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Published: 13 July 2022
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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