Optimal frequency bands for pupillography for maximal correlation with HRV.

Assessing cognitive load using pupillography frequency features presents a persistent challenge due to the lack of consensus on optimal frequency limits. This study aims to address this challenge by exploring pupillography frequency bands and seeking clarity in defining the most effective ranges for cognitive load assessment. From a controlled experiment involving 21 programmers performing software bug inspection, our study pinpoints the optimal low-frequency (0.

EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks.

How Reliable Are Ultra-Short-Term HRV Measurements during Cognitively Demanding Tasks?

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments.

Software Bug Detection Causes a Shift From Bottom-Up to Top-Down Effective Connectivity Involving the Insula Within the Error-Monitoring Network.

The neural correlates of software programming skills have been the target of an increasing number of studies in the past few years. Those studies focused on error-monitoring during software code inspection. Others have studied task-related cognitive load as measured by distinct neurophysiological measures.

Can EEG Be Adopted as a Neuroscience Reference for Assessing Software Programmers' Cognitive Load?

An emergent research area in software engineering and software reliability is the use of wearable biosensors to monitor the cognitive state of software developers during software development tasks. The goal is to gather physiologic manifestations that can be linked to error-prone scenarios related to programmers' cognitive states. In this paper we investigate whether electroencephalography (EEG) can be applied to accurately identify programmers' cognitive load associated with the comprehension of code with different complexity levels.

Correlated alpha activity with the facial expression processing network in a simultaneous EEG-fMRI experiment.

The relationship between EEG and fMRI data is poorly covered in the literature. Extensive work has been conducted in resting-state and epileptic activity, highlighting a negative correlation between the alpha power band of the EEG and the BOLD activity in the default-mode-network. The identification of an appropriate task-specific relationship between fMRI and EEG data for predefined regions-of-interest, would allow the transfer of interventional paradigms (such as BOLD-based neurofeedback sessions) from fMRI to EEG, enhancing its application range by lowering its costs and improving its flexibility.

A low-complex multi-channel methodology for noise detection in phonocardiogram signals.

The phonocardiography (PCG) is an important technique for the diagnosis of several heart conditions. However, the PCG signal is highly prone to noise, which can be an obstacle for the detection and interpretation of physiological heart sounds. Thus, the detection and elimination of noise present in PCG signals is crucial for the accurate analysis of heart sounds, especially in p-health environments.

Assessment of cardiovascular function from multi-Gaussian fitting of a finger photoplethysmogram.

Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components.

Characterization of surrogate parameters for blood pressure regulation in neurally-mediated syncope.

Neurally Mediated Syncope (NMS) is often cited as the most common cause of syncope. It can lead to severe consequences such as injuries, high rates of hospitalization and reduced quality of life, especially in elderly populations. Therefore, information about the syncope triggers and reflex mechanisms would be of a great value in the development of a cost-effective p-health system for the prediction of syncope episodes, by enhancing patients' quality of life and reducing the incidence of syncope related disorders/conditions.

Detection of hemodynamic adaptations during impending syncope: implementation of a robust algorithm based on pulse arrival time measurements only.

Syncopes are a major public health concern since they can cause severe injuries e.g. by associated falls.

Pulse Arrival Time as surrogate for systolic blood pressure changes during impending neurally mediated syncope.

Blood pressure regulation failures cause neurally mediated syncope often resulting in a fall. A warning device might help to make patients aware of an impending critical event or even trigger the patient to perform countermeasures such as lying down or isometric exercises. We previously demonstrated that the Pulse Arrival Time (PAT) methodology is a potential approach to enable early detection of impending faints.