Eyes-closed hybrid brain-computer interface employing frontal brain activation.

Jaeyoung Shin,Han-Jeong Hwang,Klaus-Robert Müller

doi:10.1371/journal.pone.0196359

Jaeyoung Shin, Han-Jeong Hwang + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0196359

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Abstract

Brain-computer interfaces (BCIs) have been studied extensively in order to establish a non-muscular communication channel mainly for patients with impaired motor functions. However, many limitations remain for BCIs in clinical use. In this study, we propose a hybrid BCI that is based on only frontal brain areas and can be operated in an eyes-closed state for end users with impaired motor and declining visual functions. In our experiment, electroencephalography (EEG) and near-infrared spectroscopy (NIRS) were simultaneously measured while 12 participants performed mental arithmetic (MA) and remained relaxed (baseline state: BL). To evaluate the feasibility of the hybrid BCI, we classified MA- from BL-related brain activation. We then compared classification accuracies using two unimodal BCIs (EEG and NIRS) and the hybrid BCI in an offline mode. The classification accuracy of the hybrid BCI (83.9 ± 10.3%) was shown to be significantly higher than those of unimodal EEG-based (77.3 ± 15.9%) and NIRS-based BCI (75.9 ± 6.3%). The analytical results confirmed performance improvement with the hybrid BCI, particularly for only frontal brain areas. Our study shows that an eyes-closed hybrid BCI approach based on frontal areas could be applied to neurodegenerative patients who lost their motor functions, including oculomotor functions.

Highlights

Brain-computer interfaces (BCIs) have in the past enabled patients to control external devices directly without the help of muscular movements [1,2,3,4,5]
We successfully demonstrated the feasibility of an EC near-infrared spectroscopy (NIRS)-BCI system, the classification accuracy was relatively low compared to those reported in standard EEG-BCI studies [19, 23]
Our present study examines the performance of an EC hybrid EEG-NIRS BCI operated by mental arithmetic (MA) that uses only the frontal areas for a convenient system setup

Summary

Introduction

Brain-computer interfaces (BCIs) have in the past enabled patients to control external devices directly without the help of muscular movements [1,2,3,4,5]. Various BCI paradigms based on electroencephalography (EEG) have been introduced to implement BCIs for physically challenged patients. These paradigms include motor imagery [13,14,15], P300 [16,17,18], steady-state visual evoked potential (SSVEP) [19], and others. These paradigms have limitations with respect to severely motor-impaired patients such as late-stage amyotrophic lateral sclerosis (ALS).

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