NOISE REMOVAL DUE TO MOTION ARTIFACT ON FUNCTIONAL NEAR INFRARED SPECTROSCOPY SIGNALS

Technology in biomedical is growing all the time. We can now estimate our health condition just by analyzing the brain activity using brain-imaging methods such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The kinds of signals obtained from this device usually contain unwanted information signals or noises from various sources which need to be removed to obtain the original signal. In this thesis, we are analyzing fNIRS data obtained from physionet. As mentioned before, fNIRS is one of the brain-imaging methods which detects the brain activity by looking at the changes of oxy-Hemoglobin (oxy-Hb) and deoxy-Hemoglobin (deoxy-Hb) levels. This thesis proposes a method to detect and remove noise using fast independent component analysis (FastICA). FastICA is a popular algorithm of independent component analysis (ICA) which is one of the methods for blind source separation. We use this method since we are going to separate the noise from the noisy or contaminated signal, hence achieving the clean signal. The fNIRS data we have gathered contain the contaminated (noisy) signals and the clean signals from nine trials. This thesis expects the accuracy of the FastICA method to detect and remove noise. To analyze the performances of the method, we are calculate the value of mean square error (MSE), peak to signal noise ratio (PSNR) and cross-correlation from the results of the denoised signals

Measuring visual cortical oxygenation in diabetes using functional near-infrared spectroscopy

Aims: Diabetes mellitus affects about 6% of the world’s population, and the chronic complications of the disease may result in macro- and micro-vascular changes. The purpose of the current study was to shed light on visual cortical oxygenation in diabetic individuals. We then aimed to compare the haemodynamic response (HDR) to visual stimulation with glycaemic control, given the likelihood of diabetic individuals suffering from such macro- and micro-vascular insult. Methodology: Thirty participants took part in this explorative study, fifteen of whom had diabetes and fifteen of whom were non-diabetic controls. The HDR, measured as concentrations of oxyhaemoglobin [HbO] and deoxyhaemoglobin [HbR], to visual stimulation was recorded over the primary visual cortex (V1) using a dual-channel oximeter. The stimulus comprised a pattern-reversal checkerboard presented in a block design. Participants’ mean glycated haemoglobin (HbA1c) level (±SD) was 7.2±0.6% in the diabetic group and 5.5±0.4% in the non-diabetic group. Raw haemodynamic data were normalised to baseline, and the last 15 s of data from each ‘stimulus on’ and ‘stimulus off’ condition were averaged over seven duty cycles for each participant. Results: There were statistically significant differences in ∆[HbO] and ∆[HbR] to visual stimulation between diabetic and non-diabetic groups (p<0.05). In the diabetic group, individuals with type 1 diabetes displayed an increased [HbO] (p<0.01) and decreased [HbR] (p<0.05) compared to their type 2 counterparts. There was also a linear relationship between both ∆[HbO] and ∆[HbR] as a function of HbA1c level (p<0.0005). Conclusions: Our findings suggest that fNIRS can be used as a quantitative measure of cortical oxygenation in diabetes. Diabetic individuals have a larger HDR to visual stimulation compared to non-diabetic individuals. This increase in ∆[HbO] and decrease in ∆[HbR] appears to be correlated with HbA1c level

Functional near-infrared spectroscopy studies in children

Psychosomatic and developmental behavioral medicine in pediatrics has been the subject of significant recent attention, with infants, school-age children, and adolescents frequently presenting with psychosomatic, behavioral, and psychiatric symptoms. These may be a consequence of insecurity of attachment, reduced self-confidence, and peer -relationship conflicts during their developmental stages. Developmental cognitive neuroscience has revealed significant associations between specific brain lesions and particular cognitive dysfunctions. Thus, identifying the biological deficits underlying such cognitive dysfunction may provide new insights into therapeutic prospects for the management of those symptoms in children. Recent advances in noninvasive neuroimaging techniques, and especially functional near-infrared spectroscopy (NIRS), have contributed significant findings to the field of developmental cognitive neuroscience in pediatrics. We present here a comprehensive review of functional NIRS studies of children who have developed normally and of children with psychosomatic and behavioral disorders

Using Functional Near Infrared Spectroscopy (fNIRS) to study dynamic stereoscopic depth perception

The parietal cortex has been widely implicated in the processing of depth perception by many neuroimaging studies, yet functional near infrared spectroscopy (fNIRS) has been an under-utilised tool to examine the relationship of oxy- ([HbO]) and de-oxyhaemoglobin ([HbR]) in perception. Here we examine the haemodynamic response (HDR) to the processing of induced depth stimulation using dynamic random-dot-stereograms (RDS). We used fNIRS to measure the HDR associated with depth perception in healthy young adults (n = 13, mean age 24). Using a blocked design, absolute values of [HbO] and [HbR] were recorded across parieto-occipital and occipital cortices, in response to dynamic RDS. Control and test images were identical except for the horizontal shift in pixels in the RDS that resulted in binocular disparity and induced the percept of a 3D sine wave that 'popped out' of the test stimulus. The control stimulus had zero disparity and induced a 'flat' percept. All participants had stereoacuity within normal clinical limits and successfully perceived the depth in the dynamic RDS. Results showed a significant effect of this complex visual stimulation in the right parieto-occipital cortex (p < 0.01, η(2) = 0.54). The test stimulus elicited a significant increase in [HbO] during depth perception compared to the control image (p < 0.001, 99.99 % CI [0.008-0.294]). The similarity between the two stimuli may have resulted in the HDR of the occipital cortex showing no significant increase or decrease of cerebral oxygenation levels during depth stimulation. Cerebral oxygenation measures of [HbO] confirmed the strong association of the right parieto-occipital cortex with processing depth perception. Our study demonstrates the validity of fNIRS to investigate [HbO] and [HbR] during high-level visual processing of complex stimuli

Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation

Accurately modelled computer-generated data can be used in place of real-world signals for the design, test and validation of signal processing techniques in situations where real data is difficult to obtain. Bio-signal processing researchers interested in working with fNIRS data are restricted due to the lack of freely available fNIRS data and by the prohibitively expensive cost of fNIRS systems. We present a simplified mathematical description and associated MATLAB implementation of model-based synthetic fNIRS data which could be used by researchers to develop fNIRS signal processing techniques. The software, which is freely available, allows users to generate fNIRS data with control over a wide range of parameters and allows for fine-tuning of the synthetic data. We demonstrate how the model can be used to generate raw fNIRS data similar to recorded fNIRS signals. Signal processing steps were then applied to both the real and synthetic data. Visual comparisons between the temporal and spectral properties of the real and synthetic data show similarity. This paper demonstrates that our model for generating synthetic fNIRS data can replicate real fNIRS recordings

Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses `Region of Interest\u27 based feature extraction. Experiments show that this method outperforms the F1 score achieved previously in classifying `low\u27 vs `high\u27 valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system

Functional Near Infrared Spectroscopy (fNIRS) synthetic data generation

Accurately modelled computer-generated data can be used in place of real-world signals for the design, test and validation of signal processing techniques in situations where real data is difficult to obtain. Bio-signal processing researchers interested in working with fNIRS data are restricted due to the lack of freely available fNIRS data and by the prohibitively expensive cost of fNIRS systems. We present a simplified mathematical description and associated MATLAB implementation of model-based synthetic fNIRS data which could be used by researchers to develop fNIRS signal processing techniques. The software, which is freely available, allows users to generate fNIRS data with control over a wide range of parameters and allows for fine-tuning of the synthetic data. We demonstrate how the model can be used to generate raw fNIRS data similar to recorded fNIRS signals. Signal processing steps were then applied to both the real and synthetic data. Visual comparisons between the temporal and spectral properties of the real and synthetic data show similarity. This paper demonstrates that our model for generating synthetic fNIRS data can replicate real fNIRS recordings

Monitoring Attentional State Using Functional Near Infrared Spectroscopy

No abstract availabl

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in realtime. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensor