文章基本信息

标题：Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification
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作者：Karel Roots ; Yar Muhammad ; Naveed Muhammad 等
期刊名称：Computers
电子版ISSN：2073-431X
出版年度：2020
卷号：9
期号：3
页码：72-80
DOI：10.3390/computers9030072
出版社：MDPI Publishing
摘要：Brain–computer interfaces (BCIs) can help people with limited motor abilities to interact with their environment without external assistance. A major challenge in electroencephalogram (EEG)-based BCI development and research is the cross-subject classification of motor imagery data. Due to the highly individualized nature of EEG signals, it has been difficult to develop a cross-subject classification method that achieves sufficiently high accuracy when predicting the subject’s intention. In this study, we propose a multi-branch 2D convolutional neural network (CNN) that utilizes different hyperparameter values for each branch and is more flexible to data from different subjects. Our model, EEGNet Fusion, achieves 84.1% and 83.8% accuracy when tested on the 103-subject eegmmidb dataset for executed and imagined motor actions, respectively. The model achieved statistically significantly higher results compared with three state-of-the-art CNN classifiers: EEGNet, ShallowConvNet, and DeepConvNet. However, the computational cost of the proposed model is up to four times higher than the model with the lowest computational cost used for comparison.
关键词：brain–computer interface (BCI); convolutional neural network (CNN); deep learning; electroencephalography (EEG); fusion network; motor imagery (MI) brain–computer interface (BCI) ; convolutional neural network (CNN) ; deep learning ; electroencephalography (EEG) ; fusion network ; motor imagery (MI)