- Research
- Open Access

# Adaptive filtering of physiological noises in fNIRS data

- Hoang-Dung Nguyen
^{2}, - So-Hyeon Yoo
^{1}, - M. Raheel Bhutta
^{3}and - Keum-Shik Hong
^{1, 4}Email authorView ORCID ID profile

**17**:180

https://doi.org/10.1186/s12938-018-0613-2

© The Author(s) 2018

**Received:**10 July 2018**Accepted:**27 November 2018**Published:**4 December 2018

## Abstract

The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.

## Keywords

- Functional near-infrared spectroscopy (fNIRS)
- Hemodynamic response (HR)
- Recursive least squares estimation (RLSE)
- Exponential forgetting
- Real time estimation
- State space model

## Introduction

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical brain imaging technique that measures oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations in the brain (or body) [1]. A continuous wave fNIRS (CW-fNIRS) system detects a brain activity based on the intensity changes of the detected light while a constant intensity of the incident light is continuously exposed on the brain. The incident light from the emitters placed on the subject’s scalp penetrates several layers of brain tissue (i.e., the scalp, skull, cerebrospinal fluid, gray matter, and white matter), during which some photons are deflected/scattered; pass through the layers, and are detected by a detector (i.e., optode, photodiode) positioned on the scalp approximately at a distance in the range of 0.5–5.5 cm from the emitters [2, 3]. The intensity changes in the detected lights are then utilized to compute the changes of HbO and HbR by using the modified Beer–Lambert law (MBLL) [1, 4–9]. The advantages of the fNIRS technique are non-invasive, inexpensive, quiet, harmless, and portable. It is especially promising for real-time and mobile applications (e.g., daily living environment, intensive aerobic exercises) [10–13]. Recently fNIRS has been shown as appropriate for neuronal activity detection [14–16] and brain–computer interface (BCI) [7, 17–20].

Event-related fNIRS signals are normally contaminated by physiological noises (i.e., heartbeat, respiration, and Mayer waves), extra-cortical physiological noises from the superficial layers, and motion artifacts. This leads to inaccuracies in the obtained cortical activity data. Therefore, it is necessary to remove these noises prior to analyzing the targeted brain functions [21]. Several approaches to reduce these noises were applied in extant studies.

In relation to the motion artifacts that normally originate from the subject’s body movement during the experimentation, various methods including Wiener filtering [22], correlation-based signal correction [23], wavelet transform [24], combined moving average and wavelet [25], an autoregressive model [26], spline interpolation [27], independent component analysis (ICA) [28], targeted principle component analysis (tPCA) [29], and kurtosis-based wavelet filtering [30] were utilized for removal, reduction, and/or correction. Specifically, the wavelet transform [24], tPCA [29], and kurtosis-based wavelet transform [30] are associated with increasing effectiveness when compared with other methods in the reduction of motion artifacts. Least mean squares (LMS) approaches were intended to reduce physiological noises from the superficial scalp and skull layers [31, 32]. Also a recursive least-square estimator (RLSE) was demonstrated in Zhang et al. [33] with fNIRS data generated by Monte Carlo simulation (no experimental data), in which the optode configuration including short- and long- separation channels was utilized to get both superficial and brain tissue components from the five-layer slab model. The RLSE algorithm is faster computationally than the LMS method. In addition, it was demonstrated that a short-separation channel of less than 9 mm was robust to the signal variation in the superficial layer. For investigating noise reduction, various approaches (i.e., band-pass filtering, correlation-based signal improvement, median filtering, Savitzky–Golay filtering, wavelet denoising, and ICA) have been pursued [34].

Recently, physiological noises (approximately 0.1 Hz for Mayer wave, 0.25 Hz for respiration, and 1 Hz for a heartbeat) have been modeled as a sum of sinusoidal functions [10, 35–37] and estimated by using a general linear model (GLM) [10, 38] or an autoregressive moving average model with external inputs [37]. Specifically, Prince et al. [35] proposed a physiological noise model consisting of three sine and cosine functions in which the frequencies of heartbeat, respiration, and Mayer wave were assumed as known. The amplitudes of the sine and cosine functions are estimated by using the extended Kalman filter. In a manner similar to Prince et al. [35], Abdelnour and Huppert [10] used three sinusoidal functions in their physiological noise model. It is noted that Scarpa et al. [36] used the same model of Prince et al. for physiological noise removal although the number of sine/cosine components varied based on the provided data, and it is claimed that the amplitudes of these components (in addition to the Kalman filtering technique) were estimated by using the least-square estimation method [36]. To obtain the corrected signals, the estimated physiological noise signal was subtracted from the acquired signals. Subsequently, the corrected signal of each trial was filtered by a Bayesian approach to improve the accuracy of the HR estimation. Specifically, diverse adaptive filtering approaches [10, 21, 31, 32, 35, 36, 38] are used for other noise reductions.

An emitter-detector pair of distance less than 1 cm is termed as a short-separation (SS) channel. This type of SS channel is used to acquire the noise in the superficial layer because the detected light in this case passes only through the superficial layer and does not reflect any cognitive activity [36, 39–44]. Saager and Berger [39] and Saager et al. [39, 41] suggested a method to reduce superficial noises by subtracting one SS measurement from a long-separation measurement. Additionally, Zhang et al. [33] demonstrated that a RLSE based adaptive method significantly reduced the superficial noises. In their study, the superficial noise estimated through the coefficient of one SS channel within the framework of RLSE. Recently, the long-separation measurement has been modeled as a linear form consisting of the expected HR and SS measurement [40, 42, 43]. It is noted that the weights of canonical functions (i.e., a combination of 15 Gaussian functions) and the amplitudes of SS data were estimated by using the Kalman filter approach. Their proposed method revealed a significant improvement in both HbOs and HbRs when compared to those obtained by the traditional adaptive filter or the standard GLM model. Additionally, Sato et al. [44] estimated the extra-cortical signal by using the GLM model, and subsequently, the corrected HR was obtained by subtracting the estimated extra-cortical signal from the measured long-separation channel signal.

Clearly, fNIRS data are contaminated by extra-cortical noises from the extra-cortical layers that occur when the light travels through the extra-cortical layers (i.e., scalp, skull, cerebrospinal fluid) prior to/after reaching the cortical layers (i.e., gray and white matter). Superficial noises are exposed in either single-SS [42] or double-SS measurement [43]. The fNIRS data obtained by the SS detectors (emitter-detector pair distance: 0.5 cm) contains extra-cortical physiological noises while data obtained by the long-separation detectors (emitter-detector pair distance: approximately 3 cm) contains HR information for both extra-cortical and cortical tissues [39, 42]. The SS measurements have been included in the GLM model involving the expected brain HR [42, 43], and the proposed Kalman estimator method obtained the efficiency of noise reduction up to 50 and 100% [42]. Additionally, Gagnon et al. [43] demonstrated that the use of the double-SS measurement reduces noises in the HbO and HbR by 59% and 47%, respectively.

In the fNIRS field, the canonical HR functions were usually generated by a combination of gamma functions [45, 46]. However, the state-space model developed in [46] is specifically convenient when compared with the use of canonical HR functions in which the impulse HR for an impulse stimulation at a specific cortex was reconstructed as a state-space equation by using the subspace identification method. It should be noted that the shapes of impulse HRs in individual cortices are different. Thus, the expected HR for an arbitrary stimulus is generated online (or in real-time).

In the study, we propose an adaptive-filter-based method to reduce physiological and superficial noises in fNIRS data. The mathematical model for filtering is a linear form comprised of the following four main components: the expected HR, SS data, the sum of sinusoidal functions representing physiological noises, and the baseline drift. The expected HR is generated with given stimuli by using the state-space model developed in [46]. The SS data (emitter-detector distance: 0.5 cm) are utilized to obtain the extra-cortical noise from the superficial layer. The physiological noises are modeled as a sum of three sinusoidal functions by following the method developed in [10, 35, 36]. In order to estimate the baseline value, the corresponding element in the regression vector is set to unity although its coefficient *b*_{0} (i.e., *b*_{0} × 1) is estimated (see “Methodology” section for more details). The unknown parameter vector in the proposed model is estimated by using the RLSE with an exponentially forgetting factor. Finally, the efficacy of the proposed method is demonstrated by using experimental right-finger-movement fNIRS data obtained from the left motor cortex. Our experimental results indicate that the proposed method significantly reduces physiological and superficial noises when compared with the existent approaches. Thus, it is possible to apply the proposed method to remove noises in both offline and online cases.

## Methodology

### Theory (brain activity model)

*t*denotes the discrete time,

*y*(

*t*) represents the measured HR signal acquired by a pair of long-separation optodes (for e.g., E-D

_{2}pair in Fig. 1) at time

*t*;

*u*(

*t*) denotes the expected HR generated by a state-space model [46]; Δ

*u*(

*t*) and Δ

^{2}

*u*(

*t*) denote the first and second derivatives of

*u*(

*t*), respectively; \(y_{\text{SS}} (t)\) (in which the subscript SS refers to short-separation) denotes the extra-cerebral physiological noise in the superficial layer (e.g., the channel E-D

_{1}in the emitter side in Fig. 1);

*f*

_{m}denotes the frequencies of physiological noises;

*q*denotes the total number of physiological noises (in this study,

*q*is set to 3);

*y*

_{b}denotes the term introduced to correct the baseline per trial;

*ε*(

*t*) denotes the white Gaussian noise, and

*a*

_{1},

*a*

_{2},…

*a*

_{4},

*b*

_{1},

*b*

_{2},…,

*b*

_{q}, and

*b*

_{0}denote unknown coefficients that are to be estimated. In extant studies, three important frequencies of physiological noises are 1 Hz (cardiac), 0.25 Hz (respiratory), and 0.1 Hz (low frequency arterial blood pressure oscillation) [47, 48]. In the study, these three frequencies were estimated with the

*fft*function available in MATLAB (MathWorks Inc.) from the data of the initial resting state data.

*ϕ*(

*t*) = [

*u*(

*t*) Δ

*u*(

*t*) Δ

^{2}

*u*(

*t*)

*y*

_{SS}(

*t*) sin(2π

*f*

_{1}

*t*) sin(2π

*f*

_{2}

*t*) … sin(2π

*f*

_{q}

*t*) 1]

^{T}denotes the regression vector,

*β*(

*t*) = [

*a*

_{1}

*a*

_{2}…

*a*

_{4}

*b*

_{1}…

*b*

_{q}

*b*

_{0}]

^{T}denotes the unknown coefficient vector, and the superscript

*T*denotes the transpose operator. Additionally,

*β*(

*t*) is estimated by the RLSE approach. According to previous works in the control field, the RLSE algorithm gives a good performance in parameter estimation [49–52] and could be utilized in real-time applications [53–56]. Therefore, this algorithm is chosen to estimate unknown parameter vector

*β*(

*t*). Thus, by using Eq. (2), the estimated brain activity is represented as follows:

*P*(

*t*) denotes the covariance matrix. We assume that \(e(t) = y(t) - \varphi^{T} \hat{\beta }(t)\) is the estimation error.

Figure 1 illustrates a schematic for brain activity estimation. The detectors’ light intensities are acquired by dual-wavelength CW-fNIRS (i.e., 760 and 830 nm). We assume that *s*(*t*) is the (arbitrary) stimuli that activates a certain brain region, which corresponds the input signal to the state-space model. The output of the box is termed as the expected/desired HR because we expect the HR to exhibit this type of a response. Additionally, its first and second derivatives are used in Eq. (1) as components in the regression vector. The difference between the measured data and the estimated data (i.e., *e*(*t*)) was utilized in updating the parameter vector \(\hat{\beta }(t)\), see (4). In the present study, the proposed method was applied to detect right-finger-movements in the left motor cortex.

*f*

_{m}vs. \(\hat{f}_{m}\), fixed and estimated). First, to eliminate a possible contribution of superficial noises in the comparison, the SS channels are ignored. Therefore, the two following models are utilized.

It is noted that the fixed frequencies of *f*_{m} (0.1 Hz, 0.25 Hz, and 1 Hz) in (5) were obtained from [10, 37] and the estimated frequencies \(\hat{f}_{m}\) in (6) were obtained from the measured data during the resting state by using the *fft* function available in MATLAB.

The main objective of the current study is to reduce both physiological and superficial noises. In addition, \(\hat{a}_{1} u(t) + \hat{a}_{2} \Delta u(t) + \hat{a}_{3} \Delta^{2} u(t)\) from (3) is extracted for the estimated HR. It is noted that the physiological noise frequencies are estimated on-line and are subsequently included in (3). The parameters are estimated using the RLSE approach. Both numerical and real experimental data are processed. The RLSE method is utilized to estimate the weights of the linear combination of the expected HR and physiological noises (i.e., heart and respiratory waves).

### Numerical validation of RLSE

^{−4}. The mixed signal of five signals is shown in Fig. 2f. In the process, the values of the amplitudes and frequencies of physiological noises were adopted from [36]. The proposed method of Eqs. (1)–(4) is applied to the mixed signal to estimate the original five signals as shown in Fig. 2a

^{*}and a

^{**}. Specifically, the estimated HbO (the red thick curve) in Fig. 2a

^{**}was obtained by computing the first three terms, i.e., \(\hat{a}_{1} u(t) + \hat{a}_{2} \Delta u(t) + \hat{a}_{3} \Delta^{2} u(t)\). Figures 2b

^{*}–d

^{*}correspond to the estimated physiological noises of Fig. 2b–d, respectively. Figures 2b

^{**}–d

^{**}denote the spectra of Fig. 2b

^{*}–d

^{*}, respectively. As shown in Fig. 2a

^{**}, the estimated HbO (the red curve) is gradually updated to the desired HbO (the blue curve in Fig. 2a). Furthermore, the estimated frequencies of cardiac (0.9 Hz), respiratory (0.26 Hz), and Mayer (0.13 Hz) waves (see Fig. 2b

^{**}–d

^{**}) are sufficiently close to the known ones in Fig. 2b–d, respectively. The result demonstrates that the proposed method effectively extracts the correct HbO and the physiological noises.

### Experimental paradigm design and verification

*1*–

*2*,

*3*–

*4*,

*5*–

*6*, and

*7*–

*8*are SS channels (0.5 cm apart). The long separation channels (i.e., approximately 1.5–4.0 cm apart) are numbered in plain text underneath, i.e.,

*1*–

*4*,

*1*–

*5*,

*1*–

*6*,

*1*–

*7*, and

*1*–

*8*.

In Ch. 1, which is composed of emitter *1* and detector *4*, two SS measurements are considered (i.e., *1*–*2* or *3*–*4*). Either measurement is included in Eq. (1). Five long-separation channels are considered when optode *1* emits light. Similarly, an additional five channels are formed from the right to the left when optode *8* shoots light. Therefore, ten channels are created in each row. In the study, only a total of 40 channels (eight channels for five distances: 1.5, 2.5, 3.0, 3.5, and 4.0 cm) were included in computation owing to the computation time constraints. Figure 3c illustrates the experimental paradigm to detect right-finger-movement brain activity.

#### Study participants

Five healthy male participants (mean age 36.2; range: approximately 33–37 years) with shaved hair were invited to perform experiments (thumb and little-finger movements) involving the left motor cortex. None of the subjects exhibited any neurological impairments or mental disorders. Four of the subjects were right-handed. To eliminate any interference from the external noise, the experiments were conducted in a dark and quiet room. The subjects were asked to sit comfortably on a chair and not to move their body during the experiment. Prior to starting the experiment, the subjects were carefully trained in how to move their fingers.

Figure 3c illustrates the experimental paradigm: An experiment comprised of ten trials of little-finger and thumb movements; and a trial consisted of a 10 s task and a 20 s rest. After a 20 s initial rest period prior to the first trial, each subject was asked to move their fingers (i.e., flexion and extension) for 10 s by watching the screen in which each finger randomly appeared five times. Therefore, an experiment corresponded to a total of 320 s. To increase the brain activity during the 10 s task period, the subjects were asked to move their (right) little-finger/thumb as fast as possible without paying attention to the number of flexions/extensions and to relax during the 20 s rest period. A laptop computer with a 15-inch screen was utilized to display pictures indicating each finger. The distance between the subject’s eyes and the laptop screen was adjusted as approximately 60 cm such that the subjects could clearly see the indicated fingers. The subjects were also instructed to keep their eyes open during the experiments. During the rest period, a black screen was displayed to relax the subjects’ eyes.

The optodes in Fig. 3a were positioned over the subject’s left motor cortex to record the HRs to the right finger movements. Prior to the experiments, the nature of the experimental procedures was clearly explained to the subjects. All the experiments in the study were performed following the guidelines of the Institutional Review Board of Pusan National University, and informed consent were obtained from all the subjects based on the Declaration of Helsinki.

#### Equipment and data conversion

Dual-wavelength continuous-wave fNIRS (DYNOT, NIRx, USA) was utilized to measure the brain’s hemodynamic responses. The intensities of the detected light were converted to hemoglobin concentration changes by using the MBLL. A total of 40 channels over the left motor cortex were configured at a sampling rate of 1.81 Hz. In the present study, the recorded fNIRS data drifted in time [45, 58, 59], and thus a baseline-correction method was applied. Specifically, a 4th order polynomial was fit to the data, and the obtained curve was subtracted from the original data to remove the drift [59, 60].

### Contrast-to-noise ratio

## Results and discussion

### Physiological noises during the rest state

### Verification of the proposed method

Our main objective involved reducing the physiological and superficial noises and subsequently extracting the correct HR from fNIRS data. In extant studies, Abdelnour and Huppert [10] proposed a brain activity model including two main components, namely the expected HR and the sum of three sinusoidal functions representing the physiological noises. Their proposed method demonstrated a significant reduction in the physiological noises although the frequencies of those noises were assumed constant. In the study, we use a linear model in which the physiological noise frequencies are estimated online during the initial resting state (prior to the first trial). Thus, by using the *fft* function, they are computed at the initial 20 s resting period per channel. Additionally, those estimated frequencies are included into the *f*_{m} in Eq. (3).

A SS measurement records the extra-cortical noise in the superficial layer while a long-separation channel includes the brain HR from both the cerebral cortex and the superficial layer [41–43, 62]. In the current study, SS measurements were utilized as reference channels to remove noises in the superficial layer.

### Physiological noises between known and estimated frequencies

To investigate the effectiveness of physiological noise frequency estimation, we compared the two brain activity models in (5) and (6), respectively. In (5), three frequencies (i.e., 0.1 Hz, 0.25 Hz, and 1 Hz) corresponded to fixed constants in three sinusoidal functions. However, as shown in (6), the frequencies were estimated online from the initial 20 s resting-state period, and the estimated values per experiment were then used in the RLSE method.

### Comparison with/without the sum of sinusoidal functions

### Comparison with Kalman filter

^{−1}and 5 × 10

^{−4}, respectively, which are the same as those in Gagnon et al. [40]. Figure 13 shows a comparison of the HbOs obtained by a Kalman estimator (blue dashed curve) and the proposed method (red thick curve). The results indicate that our proposed method was comparable with the Kalman filter.

*p*= 1.04 × 10

^{−7}). In the case of LPF, the means of the obtained HRs from Subs. 1 and 4 were significantly different (

*p*= 3.1 × 10

^{−6}). However, the results from the proposed method for all five subjects were not significantly different (

*p*= 0.03). This demonstrates that the proposed approach gives the extracted HR more consistently than LPF and Kalman filter methods.

*t*-values of the HbOs against the expected HbO were computed by using the

*robustfit*function available in MATLAB [58, 59, 68]. As shown in Fig. 15, the

*t*-values obtained by using the proposed method exceed those obtained with the Kalman filter and LPF approaches for four out of five subjects.

To implement the proposed method for brain imaging, right-finger-movement tasks in the left motor cortex were performed by using a bundled-optode arrangement. In our experiment, the available sampling rate for the bundled arrangement (a total of 32 optodes) was limited to 1.81 Hz. Therefore, with respect to the acquired fNIRS data, the proposed method deduced the cardiac frequency as within approximately 0.8–0.9 Hz. the issues of measuring different brain regions and motion-artifact removal are limited in our current work. Several relevant reports proposed that motion artifacts are measured by means of an accelerometer [2, 22]. If motion artifacts are measured in this manner, then they are included in our model as a new additional component and estimated by the RLSE approach. Specifically, we expect that motion artifacts are effectively reduced in this manner. In addition, in the future works, measured fNIRS data of different brain regions will be checked using our proposed method.

Actually, the proposed method reduces noises online. Therefore, it is appropriate for BCI applications [69–79] based on effective classifiers (e.g., linear discriminant analysis, principle component analysis, and support vector machine) [80–87] and studies on cognitive functions in daily life [88, 89]. The precisely extracted commands from measured data controls external devices if noises are perfectly removed, (i.e., robot arms, wheelchairs, and prosthetic arms) [90, 91].

## Conclusions

In the study, we presented a novel adaptive-filtering-based approach to reduce physiological and superficial noises and decomposition of the HRs in fNIRS data. Our experimental results revealed that the proposed method improved the accuracy of the estimated HR and significantly reduced physiological noises. The averaged noise reductions for HbO and HbR were 77 and 99%, respectively. The results strongly suggest that the proposed model can be utilized for noise removal and HR extraction in both offline and online applications.

## Declarations

### Authors’ contributions

HDN conducted all experiments; carried out the data processing; and prepared the draft manuscript. SHY and MRB verified the data and participated in the revision stage. KSH has conceived the idea, investigated the theoretical aspects of the work, supervised all the processes from the beginning, and finalized the manuscript. All authors read and approved the final manuscript.

### Acknowledgements

The first author’s affiliation at the time of this work was Pusan National University, and would like to thank the colleagues at Pusan National University, particularly the Vietnamese students, who participated in fNIRS experiments.

### Competing interests

The authors declare that they have no competing interests.

### Availability of data and materials

The datasets used and/or analyzed in the current work are available from the corresponding author upon a reasonable request.

### Consent for publication

Not applicable.

### Ethics approval and consent to participate

All the experiments in the study were performed following the guidelines of the Institutional Review Board of Pusan National University, and informed consent were obtained from all the subjects based on the Declaration of Helsinki.

### Funding

The study was supported by the National Research Foundation (NRF) of Korea under the auspices of the Ministry of Science and ICT, Republic of Korea (Grant Nos. NRF-2017R1A2A1A17069430 and NRF-2017R1A4A1015627).

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## Authors’ Affiliations

## References

- Villringer A, Chance B. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci. 1997;20(10):435–42.View ArticleGoogle Scholar
- Virtanen J, Noponen T, Meriläinen P. Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals. J Biomed Opt. 2009;14(5):054032.View ArticleGoogle Scholar
- Parks NA. Concurrent application of TMS and near-infrared optical imaging: methodological considerations and potential artifacts. Front Hum Neurosci. 2013;7:592.View ArticleGoogle Scholar
- Delpy DT, Cope M, van der Zee P, Arridge S, Wray S, Wyatt J. Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol. 1988;33:1433–42.View ArticleGoogle Scholar
- Bhutta MR, Hong K-S, Kim B-M, Hong MJ, Kim Y-H, Lee S-H. Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water. Rev Sci Instrum. 2014;85(2):026111.View ArticleGoogle Scholar
- Tam ND, Zouridakis G. Temporal decoupling of oxy-and deoxy-hemoglobin hemodynamic responses detected figby functional near-infrared spectroscopy (fNIRS). J Biomed Eng Med Imaging. 2014;1(2):18–28.View ArticleGoogle Scholar
- Coyle SM, Ward TE, Markham CM. Brain–computer interface using a simplified functional near-infrared spectroscopy system. J Neural Eng. 2007;4(3):219–26.View ArticleGoogle Scholar
- Hu X-S, Hong K-S, Ge SS. fNIRS-based online deception decoding. J Neural Eng. 2012;9:026012.View ArticleGoogle Scholar
- Liu X, Hong K-S. Detection of primary RGB colors projected on a screen using fNIRS. J Innov Opt Health Sci. 2017;10(6):1750006.Google Scholar
- Abdelnour AF, Huppert T. Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model. Neuroimage. 2009;46(1):133–43.View ArticleGoogle Scholar
- Kamran MA, Hong K-S. Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study. J Neural Eng. 2013;10(5):056002.View ArticleGoogle Scholar
- Pires FO, dos Anjos CAS, Covolan RJM, Pinheiro FA, St Clair Gibson A, Noakes TD, et al. Cerebral regulation in different maximal aerobic exercise modes. Front Physiol. 2016;7:253.View ArticleGoogle Scholar
- Pires FO, dos Anjos CAS, Covolan RJM, Fontes EB, Noakes TD, St Clair Gibson A, et al. Caffeine and placebo improved maximal exercise performance despite unchanged motor cortex activation and greater prefrontal cortex deoxygenation. Front Physiol. 2018;9:1144.View ArticleGoogle Scholar
- Hong K-S, Naseer N. Reduction of delay in detecting initial dips from functional near-infrared spectroscopy signals using vector-based phase analysis. Int J Neural Syst. 2016;26(3):1650012.View ArticleGoogle Scholar
- Zafar A, Hong K-S. Detection and classification of three-class initial dips from prefrontal cortex. Biomed Opt Express. 2017;8(1):367–83.View ArticleGoogle Scholar
- Zafar A, Hong K-S. Neuronal activation detection using vector phase analysis with dual threshold circles: a Functional Near-Infrared Spectroscopy Study. Int J Neural Syst. 2018;24:1850031.View ArticleGoogle Scholar
- Naseer N, Hong K-S. Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface. Neurosci Lett. 2013;553:84–9.View ArticleGoogle Scholar
- Khan MJ, Hong K-S. Passive BCI based on drowsiness detection: an fNIRS study. Biomed Opt Express. 2015;6(10):4063–78.View ArticleGoogle Scholar
- Hong K-S, Naseer N, Kim Y-H. Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI. Neurosci Lett. 2015;587:87–92.View ArticleGoogle Scholar
- Naseer N, Hong MJ, Hong K-S. Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface. Exp Brain Res. 2014;232(2):555–64.View ArticleGoogle Scholar
- Piaggi P, Menicucci D, Gentili C, Handjaras G, Gemignani A, Landi A. Singular spectrum analysis and adaptive filtering enhance the functional connectivity analysis of resting state fMRI data. Int J Neural Syst. 2014;24(3):1450010.View ArticleGoogle Scholar
- Izzetoglu M, Devaraj A, Bunce S, Onaral B. Motion artifact cancellation in NIR spectroscopy using Wiener filtering. IEEE Trans Biomed Eng. 2005;52(5):934–8.View ArticleGoogle Scholar
- Cui X, Bray S, Reiss AL. Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin dynamics. Neuroimage. 2010;49(4):3039–46.View ArticleGoogle Scholar
- Brigadoi S, Ceccherini L, Cutini S, Scarpa F, Scatturin P, Selb J, et al. Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data. Neuroimage. 2014;85:181–91.View ArticleGoogle Scholar
- Hu X-S, Arredondo MM, Gomba M, Confer N, DaSilva AF, Johnson TD, et al. Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children. J Biomed Opt. 2015;20(12):126003.View ArticleGoogle Scholar
- Barker JW, Aarabi A, Huppert TJ. Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS. Biomed Opt Express. 2013;4(8):1366–79.View ArticleGoogle Scholar
- Cooper RJ, Selb J, Gagnon L, Phillip D, Schytz HW, Iversen HK, et al. A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy. Front Neurosci. 2012;6:147.View ArticleGoogle Scholar
- Gupta V, Singh D, Sharma AK. Principal component and independent component calculation of ECG signal in different posture. In: 2nd International conference on methods and models in science and technology (ICM2ST-11). AIP conference proceedings. 2011;1414(1):102–108.Google Scholar
- Yücel MA, Selb J, Cooper RJ, Boas DA. Targeted principle component analysis: a new motion artifact correction approach for near-infrared spectroscopy. J Innov Opt Health Sci. 2014;7(2):1350066.View ArticleGoogle Scholar
- Chiarelli AM, Maclin EL, Fabiani M, Gratton G. A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data. Neuroimage. 2015;112:128–37.View ArticleGoogle Scholar
- Zhang Q, Brown EN, Strangman GE. Adaptive filtering for global interference cancellation and real-time recovery of evoked brain activity: a Monte Carlo simulation study. J Biomed Opt. 2007;12(4):044014.View ArticleGoogle Scholar
- Zhang Q, Brown EN, Strangman GE. Adaptive filtering to reduce global interference in evoked brain activity detection: a human subject case study. J Biomed Opt. 2007;12(6):064009.View ArticleGoogle Scholar
- Zhang Y, Sun JW, Rolfe P. RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: a Monte Carlo study. Physiol Meas. 2012;33(6):925–42.View ArticleGoogle Scholar
- 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(10):2889–903.View ArticleGoogle Scholar
- Prince S, Kolehmainen V, Kaipio JP, Franceschini MA, Boas D, Arridge SR. Time-series estimation of biological factors in optical diffusion tomography. Phys Med Biol. 2003;48(11):1491–504.View ArticleGoogle Scholar
- Scarpa F, Brigadoi S, Cutini S, Scatturin P, Zorzi M, Dell’acqua R, et al. A reference-channel based methodology to improve estimation of event-related hemodynamic response from fNIRS measurements. Neuroimage. 2013;72:106–19.View ArticleGoogle Scholar
- Kamran MA, Hong K-S. Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding. Neurosci Lett. 2014;580:130–6.View ArticleGoogle Scholar
- Aqil M, Hong K-S, Jeong MY, Ge SS. Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity. Neuroimage. 2012;63(1):553–68.View ArticleGoogle Scholar
- Saager RB, Berger AJ. Direct characterization and removal of interfering absorption trends in two-layer turbid media. J Opt Soc Am A Opt Image Sci Vis. 2005;22(9):1874–82.View ArticleGoogle Scholar
- Gagnon L, Perdue K, Greve DN, Goldenholz D, Kaskhedikar G, Boas DA. Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling. Neuroimage. 2011;56(3):1362–71.View ArticleGoogle Scholar
- Saager RB, Telleri NL, Berger AJ. Two-detector corrected near infrared spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS. Neuroimage. 2011;55(4):1679–85.View ArticleGoogle Scholar
- Gagnon L, Cooper RJ, Yücel MA, Perdue KL, Greve DN, Boas DA. Short separation channel location impacts the performance of short channel regression in NIRS. Neuroimage. 2012;59(3):2518–28.View ArticleGoogle Scholar
- Gagnon L, Yücel MA, Boas DA, Cooper RJ. Further improvement in reducing superficial contamination in NIRS using double short separation measurements. Neuroimage. 2014;85:127–35.View ArticleGoogle Scholar
- Sato T, Nambu I, Takeda K, Aihara T, Yamashita O, Isogaya Y, et al. Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes. Neuroimage. 2016;141:120–32.View ArticleGoogle Scholar
- Santosa H, Hong MJ, Kim S-P, Hong K-S. Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. Rev Sci Instrum. 2013;84(7):073106.View ArticleGoogle Scholar
- Hong K-S, Nguyen H-D. State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices. Biomed Opt Express. 2014;5:1778–98.View ArticleGoogle Scholar
- Julien C. The enigma of Mayer waves: facts and models. Cardiovasc Res. 2006;70(1):12–21.View ArticleGoogle Scholar
- Tong Y, Lindsey KP, Frederick BD. Partitioning of physiological noise signals in the brain with concurrent near-infrared spectroscopy and fMRI. J Cereb Blood Flow Metab. 2011;31(12):2352–62.View ArticleGoogle Scholar
- Liu Q, Ding F, Alsaedi A, Hayat T. Recursive identification methods for multivariate output-error moving average systems using the auxiliary model. Int J Control Autom Syst. 2018;16(3):1070–9.View ArticleGoogle Scholar
- Lee S-D, Jung S. Practical implementation of a factorized all pass filtering technique for non-minimum phase models. Int J Control Autom Syst. 2018;16(3):1474–81.View ArticleGoogle Scholar
- Chen J, Jiang B, Li J. Missing output identification model based recursive least squares algorithm for a distributed parameter system. Int J Control Autom Syst. 2018;16(1):150–7.View ArticleGoogle Scholar
- Oh K-S, Seo J-H. Inertial parameter estimation of an excavator with adaptive updating rule using performance analysis of Kalman filter. Int J Control Autom Syst. 2018;16(3):1226–38.View ArticleGoogle Scholar
- Lee S-D, Jung S. An adaptive control technique for motion synchronization by on-line estimation of a recursive least square method. Int J Control Autom Syst. 2018;16(3):1103–11.View ArticleGoogle Scholar
- Choi J, Kong K. Optimal sensor fusion and position control of a low-price self-driving vehicle in short-term operation conditions. Int J Control Autom Syst. 2017;15(6):2859–70.View ArticleGoogle Scholar
- Kumar A, Ojha A, Padhy PK. Anticipated trajectory based proportional navigation guidance scheme for intercepting high maneuvering targets. Int J Control Autom Syst. 2017;15(3):1351–61.View ArticleGoogle Scholar
- Thabet H, Ayadi M, Rotella F. Experimental comparison of new adaptive PI controllers based on the ultra-local model parameter identification. Int J Control Autom Syst. 2016;14(6):1520–7.View ArticleGoogle Scholar
- Soderstrom T, Stoica P. System identification. NJ: Prentice Hall Inc; 1989. p. 320–73.MATHGoogle Scholar
- Nguyen H-D, Hong K-S. Bundled-optode implementation for 3D imaging in functional near-infrared spectroscopy. Biomed Opt Express. 2016;7(9):3491–507.View ArticleGoogle Scholar
- Nguyen H-D, Hong K-S, Shin Y-I. Bundled-optode method in functional near-infrared spectroscopy. PLoS ONE. 2016;11(10):e0165146.View ArticleGoogle Scholar
- Laughner JI, Ng FS, Sulkin MS, Arthur RM, Efimov IR. Processing and analysis of cardiac optical mapping data obtained with potentiometric dyes. Am J Physiol Heart Circ Physiol. 2012;303(7):H753–65.View ArticleGoogle Scholar
- Pierro ML, Hallacoglu B, Sassaroli A, Kainerstorfer JM, Fantini S. Validation of a novel hemodynamic model for coherent hemodynamics spectroscopy (CHS) and functional brain studies with fNIRS and fMRI. Neuroimage. 2014;85:222–33.View ArticleGoogle Scholar
- Aqil M, Hong K-S, Jeong MY, Ge SS. Cortical brain imaging by adaptive filtering of NIRS signals. Neurosci Lett. 2012;514(1):35–41.View ArticleGoogle Scholar
- Thawonmas R, Cichocki A, Amari S. A cascade neural network for blind signal extraction without spurious equilibria. IEICE Trans Fund Electron Comm Comput Sci. 1998;E81A:1833–46.Google Scholar
- Zhang H, Zhang Y-J, Lu C-M, Ma S-Y, Zang Y-F, Zhu C-Z. Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements. Neuroimage. 2010;51(3):1150–61.View ArticleGoogle Scholar
- Hu X-S, Hong K-S, Ge SS. Reduction of trial-to-trial variability in functional near-infrared spectroscopy signals by accounting for resting-state functional connectivity. J Biomed Opt. 2013;18(1):017003.View ArticleGoogle Scholar
- Kalman RE. A new approach to linear filtering and prediction problems. J Basic Eng. 1960;82:35–45.View ArticleGoogle Scholar
- Hu X-S, Hong K-S, Ge SS, Jeong M-Y. Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy. Biomed Eng Online. 2010;9:82.View ArticleGoogle Scholar
- Santosa H, Hong MJ, Hong K-S. Lateralization of music processing with noises in the auditory cortex: an fNIRS study. Front Behav Neurosci. 2014;8:418.View ArticleGoogle Scholar
- Tidoni E, Gergondet P, Kheddar A, Aglioti SM. Audio-visual feedback improves the BCI performance in the navigational control of a humanoid robot. Front Neurorobot. 2014;8:20.View ArticleGoogle Scholar
- Hong K-S, Santosa H. Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy. Hear Res. 2016;333:157–66.View ArticleGoogle Scholar
- Metzger FG, Ehlis A-C, Haeussinger FB, Schneeweiss P, Hudak J, Fallgatter AJ, et al. Functional brain imaging of walking while talking: an fNIRS study. Neuroscience. 2017;343:85–93.View ArticleGoogle Scholar
- Khan MJ, Hong MJ, Hong K-S. Decoding of four movement directions using hybrid NIRS-EEG brain–computer interface. Front Hum Neurosci. 2014;8:244.Google Scholar
- Naseer N, Hong K-S. fNIRS-based brain–computer interfaces: a review. Front Hum Neurosci. 2015;9:3.Google Scholar
- Naseer N, Hong K-S. Decoding answers to four-choice questions using functional near infrared spectroscopy. J Near Infrared Spectrosc. 2015;23(1):23–31.View ArticleGoogle Scholar
- Bhutta MR, Hong MJ, Kim Y-H, Hong K-S. Single-trial lie detection using a combined fNIRS-polygraph system. Front Psychol. 2015;6:709.View ArticleGoogle Scholar
- Naseer N, Noori FM, Qureshi NK, Hong K-S. Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain–computer interface application. Front Human Neurosci. 2016;10:237.View ArticleGoogle Scholar
- Khan MJ, Hong K-S. Hybrid EEG-fNIRS-based eight-command decoding for BCI: application to quadcopter control. Front Neurorobot. 2017;11:6.View ArticleGoogle Scholar
- Hong K-S, Bhutta MR, Liu X, Shin Y-I. Classification of somatosensory cortex activities using fNIRS. Behav Brain Res. 2017;333:225–34.View ArticleGoogle Scholar
- Hong K-S, Khan MJ. Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review. Front Neurorobot. 2017;11:35.View ArticleGoogle Scholar
- Gupta V, Singh G, Mittal M, Pahuja SK. Fourier transform of untransformable signals using pattern recognition technique. In Proceedings of the second international conference on advances in computing, control, and telecommunication technologies (ACT’10). IEEE Computer Society. Jakarta, Indonesia. Dec. 2–3, 2010; p. 6–9.Google Scholar
- Gupta V, Singh R, Singh G, Singh R, Singh H. An introduction to principal component analysis and its importance in biomedical signal processing. In: 2011 international conference on life science and technology IPCBEE. IACSIT Press, Singapore. 2011; p. 3.Google Scholar
- Gupta V, Mittal M, Ojha PC, Kumar P. Principal component analysis & factor analysis as an enhanced tool of pattern recognition. Int J Elec Electr Eng Telecoms. 2015;1(2):73–8.Google Scholar
- Gupta V, Mittal M. Respiratory Signal Analysis using PCA, FFT and ARTFA. In: 2016 international conference on electrical power and energy systems (ICEPES) Maulana Azad National Institute of Technology, Bhopal, India. Dec 14–16, 2016; p. 221–225.Google Scholar
- Gupta V, Mittal M. KNN and PCA classifier with autoregressive modelling during different ECG signal interpretation. Procedia Comput Sci. 2018;125:18–24.View ArticleGoogle Scholar
- Turnip A, Hong K-S, Jeong M-Y. Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis. Biomed Eng Online. 2011;10:83.View ArticleGoogle Scholar
- Turnip A, Hong K-S. Classifying mental activities from EEG-P300 signals using adaptive neural network. Int J Innov Comput Inf Control. 2012;8(9):6429–43.Google Scholar
- Azizi A, Nourisola H, Sadeghi-Emamgholi A, Naderisafa F. Adaptive PSO-LS-wavelet H ∞ control for two-wheeled self-balancing scooter. Int J Control Autom Syst. 2017;15(5):2126–37.View ArticleGoogle Scholar
- Khan AM, Yun D-W, Ali MA, Zuhaib KM, Yuan C, Iqbal J, et al. Passivity based adaptive control for upper extremity assist exoskeleton. Int J Control Autom Syst. 2016;14(1):291–300.View ArticleGoogle Scholar
- Liu Q, Wang B, Liu Y, Zeping LV, Li W, Li Z, Fan Y. Frequency-specific effective connectivity in subjects with cerebral infarction as revealed by NIRS method. Neuroscience. 2018;373:169–81.View ArticleGoogle Scholar
- Dura-Bernal S, Zhou XL, Neymotin SA, Przekwas A, Francis JT, Lytton WW. Cortical spiking network interfaced with virtual musculoskeletal arm and robotic arm. Front Neurorobot. 2015;9:13.View ArticleGoogle Scholar
- Kocaturk M, Gulcur HO, Canbeyli R. Toward building hybrid biological/in silico neural networks for motor neuroprosthetic control. Front Neurorobot. 2015;9:8.View ArticleGoogle Scholar