Classification of multiple motor imagery using deep convolutional neural networks and spatial filters

• Published in: Applied soft computing Vol. 75; pp. 461 - 472
• Main Authors: Olivas-Padilla, Brenda E., Chacon-Murguia, Mario I.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2019
• Subjects: Brain–Computer Interface (BCI); Convolutional neural network (CNN); Electroencephalogram (EEG); Motor imagery (MI); Multi-class classification; Multi-class classification; Motor imagery (MI); Electroencephalogram (EEG); Convolutional neural network (CNN); Brain–Computer Interface (BCI)
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.11.031

Brain–Computer Interfaces (BCI) are systems that translate brain activity patterns into commands for an interactive application, and some of them recognize patterns generated by motor imagery. Currently, these systems present performances and methodologies that still are not practical enough for realistic applications. Therefore, this paper proposes two methodologies for multiple motor imagery classification. Both methodologies use features extracted by a variant of Discriminative Filter Bank Common Spatial Pattern (DFBCSP) presented in this paper. The frequency bands selection in this variant is carried out by a novel iterative algorithm that selects the frequency band that attains the highest classification accuracy for specific binary classification. For each binary combination of classes, a frequency band is selected. The resulting samples are then set into a matrix which feeds one or many Convolutional Neural Networks previously optimized by using a Bayesian optimization. The first methodology applies a Convolutional Neural Network (CNN) for the classification of all classes and the second is a modular network composed of four expert CNNs. In this modular network, each expert CNN performs a binary classification, and a fully connected network analyzes their results. To validate both approaches two datasets were used, the BCI competition IV dataset 2a and another presented in this paper recorded from eight subjects by using the OpenBCI device. The experimental results demonstrated an improvement in the classification accuracy over many classic intelligent recognition methods, without a high computation time in order that they can be implemented in an online application. •Solution for a multi-class motor imagery classification problem.•Optimization of the Convolutional Neural Networks by Bayesian optimization.•Development of a frequency bands selection algorithm.•Generation of a multi-motor imagery dataset including an idle state.