Hasan Ayaz

Optical brain monitoring for operator training and mental workload assessment

An accurate measure of mental workload in human operators is a critical element of monitoring and adaptive aiding systems that are designed to improve the efficiency and safety of human-machine systems during critical tasks. Functional near infrared (fNIR) spectroscopy is a field-deployable non-invasive optical brain monitoring technology that provides a measure of cerebral hemodynamics within the prefrontal cortex in response to sensory, motor, or cognitive activation. In this paper, we provide evidence from two studies that fNIR can be used in ecologically valid environments to assess the: 1) mental workload of operators performing standardized (n-back) and complex cognitive tasks (air traffic control--ATC), and 2) development of expertise during practice of complex cognitive and visuomotor tasks (piloting unmanned air vehicles--UAV). Results indicate that fNIR measures are sensitive to mental task load and practice level, and provide evidence of the fNIR deployment in the field for its ability to monitor hemodynamic changes that are associated with relative cognitive workload changes of operators. The methods reported here provide guidance for the development of strategic requirements necessary for the design of complex human-machine interface systems and assist with assessments of human operator performance criteria.

Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: empirical examples and a technological development

Functional near infrared spectroscopy (fNIRS) is a non-invasive, safe, and portable optical neuroimaging method that can be used to assess brain dynamics during skill acquisition and performance of complex work and everyday tasks. In this paper we describe neuroergonomic studies that illustrate the use of fNIRS in the examination of training-related brain dynamics and human performance assessment. We describe results of studies investigating cognitive workload in air traffic controllers, acquisition of dual verbal-spatial working memory skill, and development of expertise in piloting unmanned vehicles. These studies used conventional fNIRS devices in which the participants were tethered to the device while seated at a workstation. Consistent with the aims of mobile brain imaging (MoBI), we also describe a compact and battery-operated wireless fNIRS system that performs with similar accuracy as other established fNIRS devices. Our results indicate that both wired and wireless fNIRS systems allow for the examination of brain function in naturalistic settings, and thus are suitable for reliable human performance monitoring and training assessment.

Registering fNIR Data to Brain Surface Image using MRI templates

Functional near-infrared spectroscopy (fNIR) measures changes in the relative levels of oxygenated and deoxygenated hemoglobin and has increasingly been used to assess neural functioning in the brain. In addition to the ongoing technological developments, investigators have also been conducting studies on functional mapping and refinement of data analytic strategies in order to better understand the relationship between the fNIR signal and brain activity. However, since fNIR is a relatively new functional brain imaging modality as compared to positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), it still lacks brain-mapping tools designed to allow researchers and clinicians to easily interact with their data. The aim of this study is to develop a registration technique for the fNIR measurements using anatomical landmarks and structural magnetic resonance imaging (MRI) templates in order to visualize the brain activation when and where it happens. The proposed registration technique utilizes chain-code algorithm and depicts activations over respective locations based on sensor geometry. Furthermore, registered data locations have been used to create spatiotemporal visualization of fNIR measurements

Sliding-window motion artifact rejection for Functional Near-Infrared Spectroscopy

Functional Near-Infrared Spectroscopy (fNIR) is an optical brain monitoring technology that tracks changes in hemodynamic responses within the cortex. fNIR uses specific wavelengths of light, introduced at the scalp, to enable the noninvasive measurement of changes in the relative ratios of deoxygenated hemoglobin (deoxy-Hb) and oxygenated hemoglobin (oxy-Hb) during brain activity. This technology allows the design of portable, safe, affordable, noninvasive, and minimally intrusive monitoring systems that can be used to measure brain activity in natural environments, ambulatory and field conditions. However, for such applications fNIR signals can get prone to noise due to motion of the head. Improving signal quality and reducing noise, can be especially challenging for real time applications. Here, we study motion artifact related noise especially due to poor and changing sensor coupling. We have developed a simple and iterative method that can be used to automate the preprocessing of data to identify segments with such noise for exclusion and this method is also suitable for real time applications.

Enhancing dual-task performance with verbal and spatial working memory training: Continuous monitoring of cerebral hemodynamics with NIRS

To better understand the mechanisms by which working memory training can augment human performance we continuously monitored trainees with near infrared spectroscopy (NIRS) while they performed a dual verbal-spatial working memory task. Linear mixed effects models were used to model the changes in cerebral hemodynamic response as a result of time spent training working memory. Nonlinear increases in left dorsolateral prefrontal cortex (DLPFC) and right ventrolateral prefrontal cortex (VLPFC) were observed with increased exposure to working memory training. Adaptive and yoked training groups also showed differential effects in rostral prefrontal cortex with increased exposure to working memory training. There was also a significant negative relationship between verbal working memory performance and bilateral VLPFC activation. These results are interpreted in terms of decreased proactive interference, increased neural efficiency, reduced mental workload for stimulus processing, and increased working memory capacity with training.

Medial prefrontal cortex hyperactivation during social exclusion in borderline personality disorder

Frontal systems dysfunction and abandonment fears represent central features of borderline personality disorder (BPD). BPD subjects (n=10) and matched non-psychiatric comparison subjects (n=10) completed a social-cognitive task with two confederates instructed to either include or exclude subjects from a circumscribed interaction. Evoked cerebral blood oxygenation in frontal cortex was measured using 16-channel functional near infrared spectroscopy. BPD subjects showed left medial prefrontal cortex hyperactivation during social exclusion suggesting potential dysfunction of frontolimbic circuitry.

Textile-templated electrospun anisotropic scaffolds for regenerative cardiac tissue engineering

For patients with end-stage heart disease, the access to heart transplantation is limited due to the shortage of donor organs and to the potential for rejection of the donated organ. Therefore, current studies focus on bioengineering approaches for creating biomimetic cardiac patches that will assist in restoring cardiac function, by repairing and/or regenerating the intrinsically anisotropic myocardium. In this paper we present a simplified, straightforward approach for creating bioactive anisotropic cardiac patches, based on a combination of bioengineering and textile-manufacturing techniques in concert with nano-biotechnology based tissue-engineering stratagems. Using knitted conventional textiles, made of cotton or polyester yarns as template targets, we successfully electrospun anisotropic three-dimensional scaffolds from poly(lactic-co-glycolic) acid (PLGA), and thermoplastic polycarbonate-urethane (PCU, Bionate(®)). The surface topography and mechanical properties of textile-templated anisotropic scaffolds significantly differed from those of scaffolds electrospun from the same materials onto conventional 2-D flat-target electrospun scaffolds. Anisotropic textile-templated scaffolds electrospun from both PLGA and PCU, supported the adhesion and proliferation of H9C2 cardiac myoblasts cell line, and guided the cardiac tissue-like anisotropic organization of these cells in vitro. All cell-seeded PCU scaffolds exhibited mechanical properties comparable to those of a human heart, but only the cells on the polyester-templated scaffolds exhibited prolonged spontaneous synchronous contractility on the entire engineered construct for 10 days in vitro at a near physiologic frequency of ∼120 bpm. Taken together, the methods described here take advantage of straightforward established textile manufacturing strategies as an efficient and cost-effective approach to engineering 3D anisotropic, elastomeric PCU scaffolds that can serve as a cardiac patch.

A Methodology for Validating Artifact Removal Techniques for Physiological Signals

Artifact removal from physiological signals is an essential component of the biosignal processing pipeline. The need for powerful and robust methods for this process has become particularly acute as healthcare technology deployment undergoes transition from the current hospital-centric setting toward a wearable and ubiquitous monitoring environment. Currently, determining the relative efficacy and performance of the multiple artifact removal techniques available on real world data can be problematic, due to incomplete information on the uncorrupted desired signal. The majority of techniques are presently evaluated using simulated data, and therefore, the quality of the conclusions is contingent on the fidelity of the model used. Consequently, in the biomedical signal processing community, there is considerable focus on the generation and validation of appropriate signal models for use in artifact suppression. Most approaches rely on mathematical models which capture suitable approximations to the signal dynamics or underlying physiology and, therefore, introduce some uncertainty to subsequent predictions of algorithm performance. This paper describes a more empirical approach to the modeling of the desired signal that we demonstrate for functional brain monitoring tasks which allows for the procurement of a “ground truth” signal which is highly correlated to a true desired signal that has been contaminated with artifacts. The availability of this “ground truth,” together with the corrupted signal, can then aid in determining the efficacy of selected artifact removal techniques. A number of commonly implemented artifact removal techniques were evaluated using the described methodology to validate the proposed novel test platform.

Detecting cognitive activity related hemodynamic signal for brain computer interface using functional near infrared spectroscopy

The ideal non-invasive brain computer interface (BCI) transforms signals originating from human brain into commands that can control devices and applications. Hence, BCI provides a way for brain output that does not involve neuromuscular system. This represents an advantage for those individuals suffering from neuromuscular impairments such as amyotrophic lateral sclerosis (ALS) or various types of paralysis. In this study we propose to design a new noninvasive BCI that is based on optical means to measure brain activity by monitoring hemodynamic response. The proposed system uses functional near infrared (fNIR) spectroscopy to detect cognitive activity from prefrontal cortex elicited voluntarily by performing a mental task namely N-back test. Our findings indicate that fNIR signal correlates with cognitive tasks associated with working memory. These experimental outcomes compare favorably with previous functional magnetic resonance imaging (fMRI) and complement electroencephalogram (EEG) findings. Since fNIR can be implemented in the form of a wearable and minimally intrusive device, it also has the capacity to monitor brain activity under real life conditions in everyday environments leading the way to potential applications of fNIR in BCI development for communication and entertainment purposes.

Assessment of Cognitive Neural Correlates for a Functional Near Infrared-Based Brain Computer Interface System

Functional Near Infrared Spectroscopy (fNIR) is a promising brain imaging technology that relies on optical techniques to detect changes of hemodynamic responses within the prefrontal cortex in response to sensory, motor, or cognitive activation. fNIR is safe, non-invasive, affordable, and highly portable. The objective of this study is to determine if biomarkers of neural activity generated by intentional cognitive activity, as measured by fNIR, can be used to communicate directly from the brain to a computer. A bar-size-control task based on a closed-loop system was designed and tested with 5 healthy subjects across two days. Comparisons of the average task and rest period oxygenation changes are significantly different (p<0.01). The average task completion time (reaching +90%) decreases with practice: day1 (mean 52.3 sec) and day2 (mean 39.1 sec). These preliminary results suggest that a closed-loop fNIR-based BCI can allow for a human-computer interaction with a mind switch task.