Abstract
The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the “one-versus-one” (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs (p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.
Highlights
Brain-computer interfaces (BCIs) have recently attracted great attention as they have shown great potential as new modes of communication for individuals who have lost the ability for voluntary movements (Wolpaw et al, 2002; Allison et al, 2007; van Erp et al, 2012; Wolpaw and Wolpaw, 2012; Blankertz et al, 2016)
For motor imagery (MI), prominent power decreases in the δ- and θ-bands were observed over both frontal and central areas during the task period
The results of our study show, for the first time, the feasibility of the combined use of EEG and near-infrared spectroscopy (NIRS) to enhance the performance of ternary BCI classification
Summary
Brain-computer interfaces (BCIs) have recently attracted great attention as they have shown great potential as new modes of communication for individuals who have lost the ability for voluntary movements (Wolpaw et al, 2002; Allison et al, 2007; van Erp et al, 2012; Wolpaw and Wolpaw, 2012; Blankertz et al, 2016). Since each brain-imaging modality has its own pros and cons, combining two or more neural signal recording modalities, which is generally referred to as hybrid BCI, might enhance the overall performance of BCI (Pfurtscheller et al, 2010a; Dähne et al, 2015; Fazli et al, 2015). The appropriate combination of these two modalities has the potential to enhance the overall BCI performance and it has been already verified in many previous studies (Fazli et al, 2012; Koo et al, 2015; Yin et al, 2015; Shin et al, 2016, 2017b). The recent release of an openaccess dataset for hBCI reflects the increasing attention that this type of hBCI has garnered (Shin et al, 2017a)
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