Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features.

Rihui Li,Yingchun Zhang,Weitian Huang,Thomas Potter

doi:10.3389/fnhum.2017.00462

Abstract

Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0–1 s) along with initial hemodynamic dip information from fNIRS (0–2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.

Highlights

Brain-Computer Interface (BCI) systems, which use cortical activity to control external devices, have shown promising potential for multiple applications (Wolpaw et al, 2002)
One goal of our study was to comparatively evaluate the classification reliability of the features extracted from EEG, functional NearInfrared Spectroscopy (fNIRS), and EEG + fNIRS based on the results of the general linear model (GLM)
A classification accuracy of 100% would indicate that the two motor tasks are perfectly separable, while a classification accuracy of 50% would represent the poor performance of a random classifier in the context of the binary classification task

Summary

Introduction

Brain-Computer Interface (BCI) systems, which use cortical activity to control external devices, have shown promising potential for multiple applications (Wolpaw et al, 2002). One of the main focuses of current BCI-related research is increasing the efficiency of real-time reactions while using a convenient setup that minimizes the burden on the user. Considering factors like setup cost and time resolution is essential when choosing measurement modalities for a BCI study. Hybrid EEG-fNIRS System (Blankertz et al, 2007; Miller et al, 2010; Brunner et al, 2011), though non-invasive BCIs are usually preferable since they incur neither the expenses nor safety risks of electrode implantation. Current practice shows that EEG and fNIRS are considered the leading non-invasive BCI modalities due to their modest costs and practicality (von Lühmann et al, 2015; Lin and Hsieh, 2016; Khan and Hong, 2017)

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