Abstract
.The aim of this work is to develop an effective brain–computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively.
Highlights
1.1 Brain–Computer InterfaceA brain–computer interface (BCI) is a means of communication between the human brain and external devices
BCI systems based on MEG, ECoG, functional magnetic resonance imaging (fMRI), and EEG generally suffer from bulkiness, high cost, high sensitivity to head movements, low spatial and temporal resolution, and low signal quality. functional near-infrared spectroscopy (fNIRS)-based systems are known to be more advantageous, in that they can provide moderate temporal and spatial resolution
The signals obtained from channels over C3 show higher cortical activation of HbO over a period of 5 to 10 s during the right-hand motor execution [see Fig. 8(b)], whereas the signals over C4 have higher activation during the lefthand motor execution [see Fig. 8(c)]
Summary
1.1 Brain–Computer InterfaceA brain–computer interface (BCI) is a means of communication between the human brain and external devices. BCI systems have been developed based on invasive[16,17] as well as noninvasive[4,18] neuroimaging modalities, including electroencephalography (EEG),[18,19,20,21,22,23] magnetoencephalography (MEG),[10,24] electrocorticography (ECoG),[16] functional magnetic resonance imaging (fMRI),[25,26,27] and functional near-infrared spectroscopy (fNIRS).[12,23,28,29,30,31,32,33] BCI systems based on MEG, ECoG, fMRI, and EEG generally suffer from bulkiness, high cost, high sensitivity to head movements, low spatial and temporal resolution, and low signal quality. While a large body of previous studies have reported various features which can be used to extract the hemodynamic signal, the most commonly used features for fNIRS-based BCI are signal mean peak, and (μxi ), slope vwahriearnecesu(cσh2xif)e, aktuurretossaisre(Kcoxmi;j )p, ustkeedwanse40ss (Sxi ), μxi EQ-TARGET;temp:intralink-;e001;326;578 1⁄4 1 N XN j1⁄41 xi;j;
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