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Article

Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification

Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
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Computers 2020, 9(3), 72; https://doi.org/10.3390/computers9030072
Received: 26 June 2020 / Revised: 31 August 2020 / Accepted: 2 September 2020 / Published: 5 September 2020
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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. View Full-Text
Keywords: 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)
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MDPI and ACS Style

Roots, K.; Muhammad, Y.; Muhammad, N. Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification. Computers 2020, 9, 72. https://doi.org/10.3390/computers9030072

AMA Style

Roots K, Muhammad Y, Muhammad N. Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification. Computers. 2020; 9(3):72. https://doi.org/10.3390/computers9030072

Chicago/Turabian Style

Roots, Karel, Yar Muhammad, and Naveed Muhammad. 2020. "Fusion Convolutional Neural Network for Cross-Subject EEG Motor Imagery Classification" Computers 9, no. 3: 72. https://doi.org/10.3390/computers9030072

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