Abstract

In this study, a novel spatial filter design method is introduced. Spatial filtering is an important processing step for feature extraction in motor imagery-based brain-computer interfaces. This paper introduces a new motor imagery signal classification method combined with spatial filter optimization. We simultaneously train the spatial filter and the classifier using a neural network approach. The proposed spatial filter network (SFN) is composed of two layers: a spatial filtering layer and a classifier layer. These two layers are linked to each other with non-linear mapping functions. The proposed method addresses two shortcomings of the common spatial patterns (CSP) algorithm. First, CSP aims to maximize the between-classes variance while ignoring the minimization of within-classes variances. Consequently, the features obtained using the CSP method may have large within-classes variances. Second, the maximizing optimization function of CSP increases the classification accuracy indirectly because an independent classifier is used after the CSP method. With SFN, we aimed to maximize the between-classes variance while minimizing within-classes variances and simultaneously optimizing the spatial filter and the classifier. To classify motor imagery EEG signals, we modified the well-known feed-forward structure and derived forward and backward equations that correspond to the proposed structure. We tested our algorithm on simple toy data. Then, we compared the SFN with conventional CSP and its multi-class version, called one-versus-rest CSP, on two data sets from BCI competition III. The evaluation results demonstrate that SFN is a good alternative for classifying motor imagery EEG signals with increased classification accuracy.

Highlights

  • A Brain-Computer Interface (BCI) is an alternative method of communication between a user and system in which the user does not need to use his/her brain-muscular pathways to control an external device [1]

  • For any subject and m value, the accuracy of One Versus the Rest (OVR)-common spatial patterns (CSP) is constant for every running of the algorithm because OVR-CSP does not accept any hyper parameter and because it does not need an initial weight setting

  • The OVR-CSP accuracies for those M values are indicated with black dots in figures

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Summary

Introduction

A Brain-Computer Interface (BCI) is an alternative method of communication between a user and system in which the user does not need to use his/her brain-muscular pathways to control an external device [1]. Because it is a direct communication method with the brain and outer world, the BCI system emerges as a useful communication and control method for severely paralyzed people. In such a system, the user should generate different signal patterns with his PLOS ONE | DOI:10.1371/journal.pone.0125039. In Motor Imagery (MI)-based BCI systems, the kinaesthetic imagination of body movement results in oscillations called event-related synchronization/desynchronization in the sensorimotor cortex in the μ and β frequency bands [2, 3]

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