Abstract
A state-of-the-art brain–computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel’s correlation coefficients’ maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p < 0.0167) classification accuracies of 87.2 ± 7.0%, 88.4 ± 6.2%, and 88.1 ± 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems’ performance.
Highlights
IntroductionFunctional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique used to measure blood oxygenation changes as brain activity to develop a brain–computer interface (BCI) [1].Sensors 2020, 20, 6995; doi:10.3390/s20236995 www.mdpi.com/journal/sensorsAmong other noninvasive modalities used for BCIs like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG); applications of fNIRS are increasing steadily in the BCI community [2,3,4,5]. fNIRS is a cheap, portable, and safe optical brain imaging technique used in state-of-the-art BCI systems to control and drive external devices using brain signals [1,6]. fNIRS is used to analyse the brain at work and during complex everyday life situations [7,8,9,10]. fNIRS records brain activity as cortical blood oxygenation changes using two or more wavelengths of near-infrared lights ranging from 700 to 1000 nm
The analysis shows that both methods vary in channel selection and the total number of selected channels for a specific task
Tasks the proposed method selected major channels from the prefrontal region and the visual cortex, some other channels from the motor cortex showed a positive z-score value
Summary
Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique used to measure blood oxygenation changes as brain activity to develop a brain–computer interface (BCI) [1].Sensors 2020, 20, 6995; doi:10.3390/s20236995 www.mdpi.com/journal/sensorsAmong other noninvasive modalities used for BCIs like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG); applications of fNIRS are increasing steadily in the BCI community [2,3,4,5]. fNIRS is a cheap, portable, and safe optical brain imaging technique used in state-of-the-art BCI systems to control and drive external devices using brain signals [1,6]. fNIRS is used to analyse the brain at work and during complex everyday life situations [7,8,9,10]. fNIRS records brain activity as cortical blood oxygenation changes using two or more wavelengths of near-infrared lights ranging from 700 to 1000 nm. Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique used to measure blood oxygenation changes as brain activity to develop a brain–computer interface (BCI) [1]. FNIRS records brain activity as cortical blood oxygenation changes using two or more wavelengths of near-infrared lights ranging from 700 to 1000 nm. It measures the changes in oxy- and deoxy-hemoglobin (∆HbO and ∆HbR) using the modified Beer–Lambert law [11,12]. A state-of-the-art BCI system includes recording brain signal, noise reduction, channel selection, extracting features, classification, and an application interface [2]. These methods include the t-value method, baseline correction method, source analysis of brain activation, and others
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