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

Karel Roots, Yar Muhammad, Naveed Muhammad

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number72
Pages (from-to)1-9
Number of pages9
Issue number3
Publication statusPublished - 5 Sept 2020

Bibliographical note

Funding Information:
Funding: Naveed Muhammad has been funded by the European Social Fund via the IT Academy program.

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright 2020 Elsevier B.V., All rights reserved.


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