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J. Neur. Syst."],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:p>The convolutional neural network (CNN) has emerged as a powerful tool for decoding electroencephalogram (EEG), which owns the potential use in the event-related potential-based brain\u2013computer interface (ERP-BCI). However, the intra-individual difference of ERP makes the traditional learning models trained on static EEG data hard to decode when the EEG features vary along the time, which limits the long-time performance of the model. Addressing this problem, this study proposes a three-dimension CNN (3D-CNN)-based model to decode the ERPs dynamically. As input, the EEG is transformed into a brain topographic map stream along time. Then the 3D-CNN applies three-dimension kernels to capture the dynamical characteristic of spatial feature at several time points. Ten subjects participated in a cross-time task for 6 or 12[Formula: see text]h. The 3D-CNN shows higher accuracies and shorter computational cost than the baseline models of the 2D-CNN, the long short term memory (LSTM), the back propagation (BP), and the fisher linear discriminant analysis (FLDA) when detecting the ERPs. In addition, four schemes of the 3D-CNN are compared to explore the influence of the structure on the performance. This result demonstrates advanced robustness of the 3D-CNN kernel to the intra-individual EEG difference, helping to launch a more practical EEG decoding model for a long-time use.<\/jats:p>","DOI":"10.1142\/s0129065722500344","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T05:51:15Z","timestamp":1651989075000},"source":"Crossref","is-referenced-by-count":14,"title":["A Robust 3D-Convolutional Neural Network-Based Electroencephalogram Decoding Model for the Intra-Individual Difference"],"prefix":"10.1142","volume":"32","author":[{"given":"Mengfan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Reliability and Intelligence of Electrical Equipment, P. R. China"},{"name":"Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, School of Health Science and Biomedical Engineering, Hebei University of Technology, P. R. 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