Abstract
Functional near-infrared spectroscopy (fNIRS) is one of the latest noninvasive brain function measuring technique that has been used for the purpose of brain-computer interfacing (BCI). In this paper, we compare and analyze the effect of six most commonly used filtering techniques (i.e., Gaussian, Butterworth, Kalman, hemodynamic response filter (hrf), Wiener, and finite impulse response) on classification accuracies of fNIRS-BCI. To conclude with the best optimal filter for a specific cortical task owing to a specific cortical region, we divided our experimental tasks according to the three main cortical regions: prefrontal, motor, and visual cortex. Three different experiments were performed for prefrontal and motor execution tasks while one for visual stimuli. The tasks performed for prefrontal include rest (R) vs mental arithmetic (MA), R vs object rotation (OB), and OB vs MA. Similarly, for motor execution, R vs left finger tapping (LFT), R vs right finger tapping (RFT), and LFT vs RFT. Likewise, for the visual cortex, R vs visual stimuli (VS) task. These experiments were performed for ten trials with five subjects. For consistency among extracted data, six statistical features were evaluated using oxygenated hemoglobin, namely, slope, mean, peak, kurtosis, skewness, and variance. Combination of these six features was used to classify data by the nonlinear support vector machine (SVM). The classification accuracies obtained from SVM by using hrf and Gaussian were significantly higher for R vs MA, R vs OB, R vs RFT, and R vs VS and Wiener filter for OB vs MA. Similarly, for R vs LFT and LFT vs RFT, hrf was found to be significant p<0.05. These results show the feasibility of using hrf for effective removal of noises from fNIRS data.
Highlights
IntroductionEse systems are trained to generate control commands based on a specific set of patterns of brain signals [4]
Brain-computer interface (BCI) known as human-machine interface (HMI) or brain-machine interface (BMI) provides a communication mean between the user and external devices through a combination of hardware and software systems [1,2,3].ese systems are trained to generate control commands based on a specific set of patterns of brain signals [4].Brain signal acquisition is categorized between invasive and noninvasive techniques
Activities were categorized based on three main regions such as PFC, MC, and visual cortex (VC)
Summary
Ese systems are trained to generate control commands based on a specific set of patterns of brain signals [4]. Brain signal acquisition is categorized between invasive and noninvasive techniques. Due to surgical risks and limited access to the cortical region, noninvasive techniques are common in practice [5]. Noninvasive modalities include functional magnetic resonance interference (fMRI), functional near-infrared spectroscopy (fNIRS) [6], and electroencephalography (EEG) [4]. FNIRS is a comparatively new modality that has better spatial resolution and low artifacts, cost, and portability [4, 7]. Promising results have been shown by fNIRS-BCI [8,9,10]. Acquired brain signals for a specific task may contain noises that can contaminate signals and can effect informative data
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