Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

Sheng Ge,Dongchuan Yu,Ruimin Wang,Luigi Bianchi

doi:10.1371/journal.pone.0098019

Sheng Ge, Dongchuan Yu + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0098019

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Abstract

With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.

Highlights

The brain-computer interface (BCI) is a new communication scheme that depends on neither the brain’s normal output nerve pathways nor the muscles
The BCI system may be expensive as many amplifiers are needed
In long-term daily-life BCI usage, it is much easier to fit EEG electrodes on the forehead area because there is no hair in this area

Summary

Introduction

The brain-computer interface (BCI) is a new communication scheme that depends on neither the brain’s normal output nerve pathways nor the muscles. In most BCI research, whole-head multi-channel data are used to produce high accuracy. The preparation of the electrodes involves putting gel or paste on the scalp and fitting an electroencephalography (EEG) cap on the head. The skin needs to be prepared to deal with the hair under the electrodes. In long-term daily-life BCI usage, it is much easier to fit EEG electrodes on the forehead area because there is no hair in this area. In this study, we hypothesize that if high classification accuracy can be obtained in motor imagery tasks using only a few EEG channels or a single EEG channel from forehead electrodes, the use and application of a motor-imagery BCI system will be much easier and more convenient

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