Abstract
The investigation of attentional bias of depression based on P300 component has drawn interest within the last decades. Follow-up of previous research suggested the differential amplitudes between depression and normal controls (NCs) in response to various facial stimuli. In this paper, we used single-trials features in the occurrence of P300 to recognize depression from NCs. EEG activity was recorded from 24 patients and 29 NCs in a dot-probe task. We considered two traditionally used feature-extraction methods: ReliefF and principal component analysis (PCA). Then, the k-nearest neighbor (KNN), BFTree, C4.5, logistic regression and NaiveBayes were adopted in this study to make a performance comparison. The combination of NaiveBayes and PCA was applied to classify the P300 component evoked by sad-neutral pairs, which achieved higher classification accuracy than other classifiers. The classification accuracy was 98%. The classification results support that the P300 component of ERPs may reflect information processing of the specific response of depression to specific stimuli and may be applied as a physiologic index for aided diagnosis of depression in future research.
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Acknowledgement
This work was supported in part by the National Key Research and Development Program of China (No. 2019YFA0706200), in part by the National Natural Science Foundation of China (Nos. 61632014, 61627808, 61210010), in part by the National Basic Research Program of China (973 Program, No. 2014CB744600), in part by the Program of Beijing Municipal Science & Technology Commission (No. Z171100000117005), in part by the Typical Application Demonstration Project of Shandong Academy of Intelligent Computing Technology (No. SDAICT2081020), and in part by the Fundamental Research Funds for the Central Universities (Nos. lzujbky-2017-it74, lzujbky-2017-it75, lzujbky-2019–26).
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Zhang, W., Gong, T., Li, J., Li, X., Hu, B. (2021). A Study Based on P300 Component in Single-Trials for Discriminating Depression from Normal Controls. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_16
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