Cortical Tasks-Based Optimal Filter Selection: An fNIRS Study

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.

Prefrontal Cortex Activation During Motor Sequence Learning Under Interleaved and Repetitive Practice: A Two-Channel Near-Infrared Spectroscopy Study

“…Given that the motor task was performed in a stationary seated position, we did not expect any significant physiological influences associated with heartbeat, respiration, or blood pressure fNIRS signal. While there currently is no consensus on fNIRS data preprocessing for neuroimaging (Pfeifer et al, 2018;Klein and Kranczioch, 2019), a number of approaches have been proposed in the literature to reduce fNIRS signal contamination of cortical hemodynamic activity (Scholkmann et al, 2014;Khan et al, 2020) including bandpass filtering (Naseer and Hong, 2015;Kamran et al, 2016). The absence of bandpass filtering of the present fNIRS data does not allow us to rule out physiological noise as potentially contributing to our reported findings.…”

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

“…In the literature, researchers used mental arithmetic, visual tasks, letter padding, word generation, object rotation, motor imagery, motor execution, and music imagery as brain activities for data acquisition for fNIRS-BCI [22,40,[46][47][48][49]. In this study, motor imagery of left-and right-hand and mental arithmetic were selected as the brain activities.…”

“…Various noises like instrumental, physiological, and experimental noises contained by acquired hemodynamic signals had to be removed before feature extraction and classification [49]. Following the instructions [45] about preprocessing, ∆HbO and ∆HbR data were band-pass filtered using a fourth-order Butterworth filter with a passband of 0.03-0.15 Hz to remove physiological noises.…”

Comparing different pre-processing routines for infant fNIRS data

“…A systematic review of filtering methods in adult NIRS studies ( Pinti et al, 2019 ) compared Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) filters of different orders, and found that high order (>500) bandpass FIR filters perform best. When filters were compared in terms of their impact on classification performance in a Brain-Computer Interface study, the hemodynamic response filter ( Penny et al, 2011 ) was found to work best ( Khan et al, 2020 ).…”