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

• Autorzy: Rahman Md. Asadur , Rashid Mohd Abdur , Ahmad Mohiuddin

Identyfikatory

DOI

10.1016/j.bbe.2019.06.004

• Języki publikacji: EN

Abstrakty

EN

This paper proposes a method to find the best conditions for applying Savitzky–Golay (SG) filter to remove physiological noises from the functional near-infrared spectroscopy (fNIRS) signal. A narrative review on existing physiological noise reduction techniques from fNIRS signal demonstrates that the most common methods are window based finite impulse response (FIR) and SG filters. However, these filters did not clarify why and how it is able to remove noises from the fNIRS signal. This paper shows a systemic investigation of works performed with window based FIR filter and SG filter and found very convincing results to use SG filter with specific conditions. Three main frequency bands (0–0.1 Hz, 0–0.14 Hz, and 0.03–0.1 Hz) have been considered as standard for fNIRS signal filtering and filtered the signals by window-based FIR filter. With a number of conditions of SG filter, the raw fNIRS signals were filtered again and checked the correlation between filtered signals by FIR and SG. By check and trial basis, the best correlations were revealed. To validate the proposed results, several golden standard statistical investigations were analyzed. The experimental results propose a recommendation which indicates the best conditions of the SG filter to remove physiological noises from the fNIRS signals.

Słowa kluczowe

EN

functional near infrared spectroscopy; physiological noise; finite impulse response filter; Savitzky–Golay filter; Pearson's correlation coefficient

PL

spektroskopia w podczerwieni; filtr o skończonej odpowiedzi impulsowej; filtr Savitzky'ego-Golaya; współczynnik korelacji Pearsona

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 3
• Strony: 624--637
• Opis fizyczny: Bibliogr. 36 poz., rys., tab., wykr.

Twórcy

autor

Rahman Md. Asadur

• Department of Biomedical Engineering, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh

autor

Rashid Mohd Abdur

• Department of EEE, Noakhali Science and Technology University (NSTU), Noakhali 3814, Bangladesh

autor

Ahmad Mohiuddin

• Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh

Bibliografia

• [1] Burle B, Spieser L, Roger C, Casini L, Hasbroucq T, Vidal F. Spatial and temporal resolutions of EEG: is it really black and white? A scalp current density view. Int J Psychophysiol 2015;97(September (3)):210–20.
• [2] Basic Principles of Magnetoencephalography, MIT Class notes, available in: http://web.mit.edu/kitmitmeg/whatis.html.
• [3] Hong K, Khan MJ, Hong MJ. Feature extraction and classification methods for hybrid fNIRS–EEG brain– computer interfaces. Front Hum Neurosci 2018;12(June (246)):1–25. http://dx.doi.org/10.3389/fnhum.2018.00246.
• [4] Ayaz H, Shewokis PA, Curtin A, Izzetoglu M, Izzetoglu K, Onaral B. Using mazesuite and functional near infrared spectroscopy to study learning in spatial navigation. J Vis Exp 2011;56(October):1–11.
• [5] Devaraj A. Signal processing for functional near-infrared neuroimaging.[M.Sc. thesis] Philadelphia: Drexel University; 2010.
• [6] Izzetoglu M, Bunce SC, Izzetoglu K, Onaral B, Pourrezaei K. Functional brain imaging using near-infrared technology. IEEE Eng Med Biol Mag 2007;26:38–46.
• [7] Ayaz H, Shewokis PA, Bunce S, Izzetoglu K, Willems B, Onaral B. Optical brain monitoring for operator training and mental workload assessment. NeuroImage 2012;59:36–47.
• [8] Ayaz H, Shewokis PA, Izzetoglu M, Çakir MP, Onaral B. Tangram solved? Prefrontal cortex activation analysis during geometric problem solving. Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2012. pp. 4724–7.
• [9] Meiria H, Selaa I, Nesher P, Izzetoglu M, Izzetoglu K, Onaral B, et al. Frontal lobe role in simple arithmetic calculations: an fNIR study. Neurosci Lett 2012;510:43–7.
• [10] Fazli S, Mehnert J, Steinbrink J, Curio G, Villringer A, Müller KR, et al. Enhanced performance by a hybrid NIRS–EEG brain computer interface. NeuroImage 2012;59:519–29.
• [11] Liu Y, Ayaz H, Curtin A, Onaral B, Shewokis PA. Towards a hybrid P300-based BCI using simultaneous fNIR and EEG. Foundations of augmented cognition. Lecture notes in computer science vol 8027, Berlin, Heidelberg: Springer; 2013. p. 335–44.
• [12] Batula AM, Ayaz H, Kim YE. Evaluating a four-class motor-imagery-based optical brain–computer interface. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2014. pp. 2000–3.
• [13] Lee MH, Fazli S, Mehnert J, Lee SW. Subject-dependent classification for robust idle state detection using multi-modal neuroimaging and data-fusion techniques in BCI. Pattern Recognit 2015;48:2725–37.
• [14] Buccino AP, Keles HO, Omurtag A. Hybrid EEG-fNIRS asynchronous brain–computer interface for multiple motor tasks. PLoS One 2016;11(January (1)):1–16.
• [15] Batula AM, Mark JA, Kim YE, Ayaz H. Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci 2017;2017(May):1–12 [Article ID 5491296].
• [16] Janani A, Sasikala M. Investigation of different approaches for noise reduction in functional near-infrared spectroscopy signals for brain–computer interface applications. Neural Comput Appl 2017;28(April (10)):2889–903. http://dx.doi.org/10.1007/s00521-017-2961-4.
• [17] Hai NT, Cuong NQ, Kho TQD, Toi VV. Temporal hemodynamic classification of two hands tapping using functional near-infrared spectroscopy. Front Neurosci 2013;7(September (516)):1–12.
• [18] Strait M, Scheutz M. What we can and cannot (yet) do with functional near infrared spectroscopy. Front Neurosci 2014;8(May (117)):1–12.
• [19] Shin J, Jeong J. Multiclass classification of hemodynamic responses for performance improvement of functional near-infrared spectroscopy-based brain–computer interface. J Biomed Opt 2014;19(June (6)):067009-1–9.
• [20] Savitzky A, Golay MJE. Soothing and differentiation of data by simplifying least squares procedures. Anal Chem 1964;36:1627–39.
• [21] Schafer RW. What is a Savitzky–Golay filter? [Lecture Notes] IEEE Signal Process Mag 2011;28(July (4)):111–7.
• [22] Pfeifer MD, Scholkmann F, Labruyère R. Signal processing in functional near-infrared spectroscopy (fNIRS): methodological differences lead to different statistical results. Front Hum Neurosci 2018;11(641). http://dx.doi.org/10.3389/fnhum.2017.00641.
• [23] Vrana A, Meier ML, Hotz-Boendermaker S, Humphreys BK, Scholkmann F. Different mechanosensory stimulations of the lower back elicit specific changes in hemodynamics and oxygenation in cortical sensorimotor areas—a fNIRS study. Brain Behav 2016;6(12):1–18. http://dx.doi.org/10.1002/brb3.575.
• [24] Boashash B. Time–frequency signal analysis and processing: a comprehensive reference.second edition. Series in Signal and Image Processing, Eurasip and Academic Press; December 2015.
• [25] Bozkurt A, Rosen A, Rosen H, Onaral B. A portable near infrared spectroscopy system for bedside monitoring of newborn brain. Biomed Eng Online 2005;4(April (29)).
• [26] Mayhew J, Askew S, Zheng Y, Porrill J, Westby GWM, Redgrave P, et al. Cerebral vasomotion: a 0.1 Hz oscillation in reflected light imaging of neural activity. Neuroimage 1996;4:183–93.
• [27] Elwell CE, Springett R, Hillman E, Delpy DT. Oscillations in cerebral hemodynamics: implications for functional activation studies. Adv Exp Med Biol 1999;471:57–65.
• [28] Obrig H, Neufang M, Wenzel R, Kohl M, Steinbrink J, Einhaupl K, et al. Spontaneous low frequency oscillations of cerebral hemodynamics and metabolism in human adults. Neuroimage 2000;12:623–39.
• [29] Lundberg N, Troupp H, Lorin H. Continuous recordings and control of ventricular fluid pressure in neurosurgical practice. J Neurosurg Exp Neurol 1962;22(6):581–90. http://dx.doi.org/10.1097/00005072-196207000-00018.
• [30] Proakis JG, Manolakis DG. Digital signal processing: principles, algorithms, and applications. third edition. New Delhi: Prentice-Hall of India Private Limited; 2002.
• [31] Spiegel MR, Steohens LJ. ‘‘Statistics,’’ third edition, Schaum's outline series. McGraw-Hill; 2003–2004.
• [32] Ayaz H, Izzetoglu M, Shewokis PA, Onaral B. Sliding-window motion artifact rejection for functional near-infrared spectroscopy. 32nd Annual International Conference of IEEE EMBS; 2010. pp. 6567–70.
• [33] World Medical Association Declaration of Helsinki – ethical principles for medical research involving human subjects. Adopted by 64th WMA General Assembly; Clinical Review & Education October 2013.
• [34] Ayaz H. Analytical software and stimulus-presentation platform to utilize, visualize and analyze near-infrared spectroscopy measures.[Master's thesis] Philadelphia: Drexel University; 2005.
• [35] Rahman MA, Ahmad M. Movement related events classification from functional near infrared spectroscopic signal. 19th International Conference on Computer and Information Technology (ICCIT); 2016. pp. 207–12.
• [36] Ayaz H. Functional near infrared spectroscopy based brain computer interface.[Ph.D. thesis] Philadelphia: Drexel University; 2010.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).