Paper Info
Title
The Mechanism of a Multi-Branch Structure for EEG-Based Motor Imagery Classification.
Abstract
In the field of motor imagery (MI) classification, it is a great challenge to preserve temporal features and spatial ones of the electroencephalogram (EEG) data simultaneously for effective processing. In our former study, a novel framework for MI classification has been proposed, which represents the EEG data into a 3D form and handles it with a multi-branch 3D convolutional neural network (3D CNN). The 3D CNN is constructed with three different branches and already proved to be better than one branch network. In this paper, to study the mechanism of the multi-branch structure, we modify the framework of the multi-branch network with three same networks as the branch and compare different multi-branch networks with the corresponding one branch network to find whether multi-branch structure is better than one branch structure in MI classification tasks. Experiment results revealed that the modified multi-branch 3D CNN can also reach state-of-the-art classification kappa value level and performs better than other methods in terms of standard deviation including the original network with three different branches. The good performance of the modified network may reflect multi-branch structure is better than one branch structure and shows its application value in EEG classification.
Year
DOI
Venue
2019
10.1109/ROBIO49542.2019.8961384
ROBIO
Field
DocType
Citations 
Eeg classification,Pattern recognition,Convolutional neural network,Control engineering,Artificial intelligence,Eeg data,Engineering,Branch structure,Standard deviation,Electroencephalography,Motor imagery
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Haifeng Zhou100.34
Xinqiao Zhao200.34
Hongmiao Zhang300.34
Shaolong Kuang404.06