Selecting the optimal conditions of Savitzky–Golay filter for fNIRS signal

Rahman, Md. Asadur; Rashid, M. A.; Ahmad, Mohiuddin

doi:10.1016/j.bbe.2019.06.004

Cited by 37 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Savitzky–Golay smoothing is mostly employed to smooth over spike MAs [ 50 ]. It can also be used to smooth physiological noise in the data; however, the reasons for why this filter is appropriate are unclear in this circumstance [ 51 ]. This type of filter uses a least-squares polynomial to fit the fNIRS data within a certain window while preserving some higher frequencies [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

2021

View full text Add to dashboard Cite

FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.

show abstract

Section: Resultsmentioning

confidence: 99%

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

2021

View full text Add to dashboard Cite

show abstract

“…To avoid phase delay in filtering, the built-in MATLAB® command ’filtfilt’ was used. Furthermore, smoothing of the fNIRS signal was done by applying the Savitzky-Golay filter with the optimal order and frame size recommended in [ 34 ]. In [ 34 ], the recommended filter order and frame size is three and nineteen, respectively, for a frequency band of 0.03–0.1 Hz.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Furthermore, smoothing of the fNIRS signal was done by applying the Savitzky-Golay filter with the optimal order and frame size recommended in [ 34 ]. In [ 34 ], the recommended filter order and frame size is three and nineteen, respectively, for a frequency band of 0.03–0.1 Hz. We used the same order and frame size because our band of frequencies are quite similar.…”

Section: Materials and Methodsmentioning

confidence: 99%

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals

Khan

Noori

Yazidi

et al. 2021

Sensors

View full text Add to dashboard Cite

Functional near-infrared spectroscopy (fNIRS) is a comparatively new noninvasive, portable, and easy-to-use brain imaging modality. However, complicated dexterous tasks such as individual finger-tapping, particularly using one hand, have been not investigated using fNIRS technology. Twenty-four healthy volunteers participated in the individual finger-tapping experiment. Data were acquired from the motor cortex using sixteen sources and sixteen detectors. In this preliminary study, we applied standard fNIRS data processing pipeline, i.e., optical densities conversation, signal processing, feature extraction, and classification algorithm implementation. Physiological and non-physiological noise is removed using 4th order band-pass Butter-worth and 3rd order Savitzky–Golay filters. Eight spatial statistical features were selected: signal-mean, peak, minimum, Skewness, Kurtosis, variance, median, and peak-to-peak form data of oxygenated haemoglobin changes. Sophisticated machine learning algorithms were applied, such as support vector machine (SVM), random forests (RF), decision trees (DT), AdaBoost, quadratic discriminant analysis (QDA), Artificial neural networks (ANN), k-nearest neighbors (kNN), and extreme gradient boosting (XGBoost). The average classification accuracies achieved were 0.75±0.04, 0.75±0.05, and 0.77±0.06 using k-nearest neighbors (kNN), Random forest (RF) and XGBoost, respectively. KNN, RF and XGBoost classifiers performed exceptionally well on such a high-class problem. The results need to be further investigated. In the future, a more in-depth analysis of the signal in both temporal and spatial domains will be conducted to investigate the underlying facts. The accuracies achieved are promising results and could open up a new research direction leading to enrichment of control commands generation for fNIRS-based brain-computer interface applications.

show abstract

“…IMU data were checked immediately after each session from each subject for movements during the recording, and the sessions that showed movements were discarded. The raw NIR signals were low-pass filtered with a third-order Butterworth bandpass filter with a 0.01–0.5 Hz cut-off frequency [ 42 ], and afterward, a two-second windowed moving-average filter was applied to further remove any physiological interference in the detected NIR signal, such as Mayer waves, respiration and heart rate [ 43 , 44 ]. Afterward, the modified Beer–Lambert [ 45 ] law was applied to convert the multiband raw NIR signal to oxygenation change signals, known as the change in oxygenated hemoglobin (ΔHbO2) and deoxygenated hemoglobin (ΔHbR).…”

Section: Methodsmentioning

confidence: 99%

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability

Siddiquee

Atri

Marquez

et al. 2020

Sensors

View full text Add to dashboard Cite

Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators’ cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.

show abstract

Selecting the optimal conditions of Savitzky–Golay filter for fNIRS signal

Cited by 37 publications

References 14 publications

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

Classification of Individual Finger Movements from Right Hand Using fNIRS Signals

Sensor Location Optimization of Wireless Wearable fNIRS System for Cognitive Workload Monitoring Using a Data-Driven Approach for Improved Wearability

Contact Info

Product

Resources

About