Abstract
Decision confidence can reflect the correctness of people’s decisions to some extent. To measure the reliability of human decisions in an objective way, we introduce a spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) for recognizing decision confidence levels based on EEG signals in this paper. The advantage of our proposed method is that it fully utilizes the knowledge from the spectral, spatial, and temporal dimensions of the EEG signals. The experiments based on a confidence text exam task within limited time are designed and conducted. The experimental results demonstrate that the SST-AGCN enhances the performance compared with the models without using the spatial or temporal information for classifying five decision confidence levels, achieving the average F1-score of 57.92% and the average accuracy of 58.16%. As for the two extreme confidence levels, the average F1-score reaches to 93.17% with the average accuracy of 94.11%. Furthermore, the neural patterns of decision confidence are analyzed in this paper through the brain topographic maps and the learned functional connectivities by the SST-AGCN. The experimental results indicate that the delta, theta and alpha bands may be critical in measuring human decision confidence levels with better recognition performance than other frequency bands.
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Acknowledgments
This work was supported in part by grants from the National Natural Science Foundation of China (No. 61976135), MOST 2030 Brain Project (No. 2022ZD0208500), Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX), SJTU Global Strategic Partnership Fund (2021 SJTU-HKUST), Shanghai Marine Equipment Foresight Technology Research Institute 2022 Fund (No. GC3270001/012), and GuangCi Professorship Program of RuiJin Hospital Shanghai Jiao Tong University School of Medicine.
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Li, R., Wang, Y., Lu, BL. (2023). Measuring Decision Confidence Levels from EEG Using a Spectral-Spatial-Temporal Adaptive Graph Convolutional Neural Network. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_34
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