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A Robust 3D-Convolutional Neural Network-Based Electroencephalogram Decoding Model for the Intra-Individual Difference.
- Source :
-
International Journal of Neural Systems . Jul2022, Vol. 32 Issue 7, p1-17. 17p. - Publication Year :
- 2022
-
Abstract
- 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–computer 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 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01290657
- Volume :
- 32
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- International Journal of Neural Systems
- Publication Type :
- Academic Journal
- Accession number :
- 157909182
- Full Text :
- https://doi.org/10.1142/S0129065722500344