Electroencephalographic Measures Research Articles

Moral conviction and metacognitive ability shape multiple stages of information processing during social decision-making

Moral conviction has the potential to inspire activism and change, but can also instigate divisiveness and great harm. Moreover, attitudes held with moral conviction are experienced as universal objective truths and are less likely to demonstrate social conformity. Some evidence suggests that holding strong moral views may be a consequence of a cognitive style which includes lower metacognitive sensitivity. This study combined measures of attitudes, moral convictions, psychophysics, and electroencephalography (EEG) to determine how metacognition and moralization influence different stages of information processing during social decision-making, as well as the neural mechanisms underlying these effects. First, 170 participants were asked to indicate their attitudes and moral convictions about specific social issues that are currently important to US college students. Later, a subset of 81 participants completed a perceptual confidence task to assess their metacognition. Then, they underwent EEG while evaluating photographs of protests ostensibly about the same social issues. Prior to each photo, participants were provided with statistical information about social consensus from their peers for each issue. As predicted, stronger moral convictions were associated with lower social conformity. Furthermore, this effect was more pronounced in individuals with lower metacognitive ability. Moralized content was prioritized in multiple stages of information processing, including both early automatic attention and later deliberative stages, with medial frontal negativity (MFN) and early posterior negativity (EPN) predicting reduced conformity. Changes in alpha and high beta power indicated increased attention and engagement for moralized content. Together, results from these complementary methods and levels of analysis both converge to indicate that moralization alters sensitivity to social influence by shifting neural responses to efficiently prioritize information related to moralized beliefs.

The differential roles of lexical and sublexical processing during spoken-word recognition in clear and in noise

Successful spoken-word recognition relies on interplay between lexical and sublexical processing. Previous research demonstrated that listeners readily shift between more lexically-biased and more sublexically-biased modes of processing in response to the situational context in which language comprehension takes place. Recognizing words in the presence of background noise reduces the perceptual evidence for the speech signal and – compared to the clear – results in greater uncertainty. It has been proposed that, when dealing with greater uncertainty, listeners rely more strongly on sublexical processing. The present study tested this proposal using behavioral and electroencephalography (EEG) measures. We reasoned that such an adjustment would be reflected in changes in the effects of variables predicting recognition performance with loci at lexical and sublexical levels, respectively. We presented native speakers of Dutch with words featuring substantial variability in (1) word frequency (locus at lexical level), (2) phonological neighborhood density (loci at lexical and sublexical levels) and (3) phonotactic probability (locus at sublexical level). Each participant heard each word in noise (presented at one of three signal-to-noise ratios) and in the clear and performed a two-stage lexical decision and transcription task while EEG was recorded. Using linear mixed-effects analyses, we observed behavioral evidence that listeners relied more strongly on sublexical processing when speech quality decreased. Mixed-effects modelling of the EEG signal in the clear condition showed that sublexical effects were reflected in early modulations of ERP components (e.g., within the first 300 msec post word onset). In noise, EEG effects occurred later and involved multiple regions activated in parallel. Taken together, we found evidence – especially in the behavioral data – supporting previous accounts that the presence of background noise induces a stronger reliance on sublexical processing.

A Preliminary Report of Network Electroencephalographic Measures in Primary Progressive Apraxia of Speech and Aphasia.

The objective of this study was to characterize network-level changes in nonfluent/agrammatic Primary Progressive Aphasia (agPPA) and Primary Progressive Apraxia of Speech (PPAOS) with graph theory (GT) measures derived from scalp electroencephalography (EEG) recordings. EEGs of 15 agPPA and 7 PPAOS patients were collected during relaxed wakefulness with eyes closed (21 electrodes, 10–20 positions, 256 Hz sampling rate, 1–200 Hz bandpass filter). Eight artifact-free, non-overlapping 1024-point epochs were selected. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and upper and lower alpha frequency bands. Differences in GT and MST measures between agPPA and PPAOS were assessed with Wilcoxon rank-sum tests. Of greatest interest, Spearman correlations were computed between behavioral and network measures in all frequency bands across all patients. There were no statistically significant differences in GT or MST measures between agPPA and PPAOS. There were significant correlations between several network and behavioral variables. The correlations demonstrate a relationship between reduced global efficiency and clinical symptom severity (e.g., parkinsonism, AOS). This preliminary, exploratory study demonstrates potential for EEG GT measures to quantify network changes associated with degenerative speech–language disorders.

Emerging Electroencephalographic Biomarkers to Improve Preclinical to Clinical Translation in Alzheimer's Disease.

Continually emerging data indicate that sub-clinical, non-convulsive epileptiform activity is not only prevalent in Alzheimer’s disease (AD) but is detectable early in the course of the disease and predicts cognitive decline in both humans and animal models. Epileptiform activity and other electroencephalographic (EEG) measures may hold powerful, untapped potential to improve the translational validity of AD-related biomarkers in model animals ranging from mice, to rats, and non-human primates. In this review, we will focus on studies of epileptiform activity, EEG slowing, and theta-gamma coupling in preclinical models, with particular focus on its role in cognitive decline and relevance to AD. Here, each biomarker is described in the context of the contemporary literature and recent findings in AD relevant animal models are discussed.

Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression.

Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.

The Effect of Juingong Meditation on the Theta to Alpha Ratio in the Temporoparietal and Anterior Frontal EEG Recordings.

(1) Background: The effect of Juingong meditation on brainwave patterns has not been explored yet. This study aimed to study the changes in brainwave patterns produced by Juingong meditation, through electroencephalography (EEG) measurements. (2) Methods: The study included 23 participants from the Hanmaum Seon Center in Korea. EEG measurements were performed using InteraXon’s four-channel EEG measurement equipment, Muse. It measures EEG patterns in the temporoparietal and anterior frontal lobes. Brainwaves were measured in two different states: when Juingong meditation was practiced and when instructed mind wandering (IMW) was practiced. The EEG recordings were analyzed using the theta/alpha index. (3) Results: In the Juingong meditation state, the power of alpha was relatively higher than that of theta and these results were valid in the temporal parietal lobe channel. This indicates that relatively more alpha waves were induced in the temporal parietal lobe when Juingong meditation was practiced. (4) Conclusions: When Juingong meditation is practiced, the theta/alpha ratio changes without delay, which means that the practical effect of Juingong meditation on brainwave patterns is immediately apparent.

Epilepsy and brain network hubs.

Epilepsy is a disorder of brain networks. A better understanding of structural and dynamic network properties may improve epilepsy diagnosis, treatment, and prognostics. Hubs are brain regions with high connectivity to other parts of the brain and are typically situated along the brain's most efficient communication pathways, supporting large-scale brain wiring and many higher order neural functions. The visualization and analysis of hubs offers a perspective on regional and global network organization and can provide novel insights into brain disorders and epilepsy. By notably supporting the interaction between various brain networks, hubs may be implicated in seizure spread and in epilepsy-related phenotypes. In this review, we will discuss the growing literature on atypical hub organization in common epilepsy syndromes, both related to neuroimaging of brain structure and function, and related to neurophysiological data from magneto- and electroencephalographic measures of neural dynamics. With studies increasingly exploring the clinical utility of network neuroscience approaches, we highlight the potential of hub mapping as a candidate biomarker of cognitive dysfunction and postsurgical seizure outcome. We will conclude the review with a discussion of current limitations and outlook for future research.

Real-Time Automated Epileptic Seizure Detection by Analyzing Time-Varying High Spatial Frequency Oscillations

Real-time seizure onset detection has been an active area of research in the study of epilepsy. Electroencephalography (EEG) measurements are widely used in seizure detection due to their high temporal resolution, cost-effective, and noninvasive nature. Various approaches based on machine learning are used for epileptic seizure detection, but these approaches do not explicitly reveal the underlying dynamics, require larger datasets for training, and are computationally demanding. Although high-frequency oscillations (HFOs) are the new biomarkers of epilepsy, they cannot be used with the existing data acquisition systems as they require high sampling rates and high cutoff frequency of the used filters. In this article, we present a novel approach for real-time seizure detection using high spatial frequencies. Since eigenvalues of the graph Laplacian represent spatial frequencies, we conjecture that higher eigenvalues and eigenvectors will contain the detailed information of seizure and non-seizure brain states. Hence, we have formed sub-band characteristic response vector (sub-band CRV) using weighted sum of eigenvectors corresponding to higher spatial frequencies and analyzed it over time. We have used a publicly available dataset to demonstrate the efficacy of the proposed approach. We observed that the proposed approach performs satisfactorily well in real-time automated seizure detection without requiring any kind of prior training. Moreover, our approach is not only accurate in seizure detection but also is independent of sampling rates, hence can be implemented easily in clinical realm for developing an automated seizure detection tool with the existing data acquisition systems operating at low sampling rates.

An Efficient Approach for Driver Drowsiness Detection at Moderate Drowsiness Level Based on Electroencephalography Signal and Vehicle Dynamics Data.

Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data. A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data. According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier. According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data.

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in- home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re- trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa =0.729$ </tex-math></inline-formula> ) for five sleep stages (W, N1, N2, N3, and R), 84.1% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa =0.773$ </tex-math></inline-formula> ) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa =0.801$ </tex-math></inline-formula> ) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in- home PSGs.

Application of wearable EEG sensors for indoor thermal comfort measurements

Multidomain comfort theories have been demonstrated to interpret human thermal comfort in buildings by employing human-centered physiological measurements coupled with environmental sensing techniques. Thermal comfort has been correlated with brain activity through electroencephalographic (EEG) measurements. However, the application of low-cost wearable EEG sensors for measuring thermal comfort has not been thoroughly investigated. Wearable EEG devices provide several advantages in terms of reduced intrusiveness and application in real-life contexts. However, they are prone to measurement uncertainties. This study presents results from the application of an EEG wearable device to investigate changes in the EEG frequency domain at different indoor temperatures. Twenty-three participants were enrolled, and the EEG signals were recorded at three ambient temperatures: cold (16 °C), neutral (24 °C), and warm (31 °C). Then, the analysis of brain Power Spectral Densities (PSDs) was performed, to investigate features correlated with thermal sensations. Statistically significant differences of several EEG features, measured on both frontal and temporal electrodes, were found between the three thermal conditions. Results bring to the conclusion that wearable sensors could be used for EEG acquisition applied to thermal comfort measurement, but only after a dedicated signal processing to remove the uncertainty due to artifacts.

Delirium, Caffeine, and Perioperative Cortical Dynamics.

Delirium is a major public health issue associated with considerable morbidity and mortality, particularly after surgery. While the neurobiology of delirium remains incompletely understood, emerging evidence suggests that cognition requires close proximity to a system state called criticality, which reflects a point of dynamic instability that allows for flexible access to a wide range of brain states. Deviations from criticality are associated with neurocognitive disorders, though the relationship between criticality and delirium has not been formally tested. This study tested the primary hypothesis that delirium in the postanesthesia care unit would be associated with deviations from criticality, based on surrogate electroencephalographic measures. As a secondary objective, the impact of caffeine was also tested on delirium incidence and criticality. To address these aims, we conducted a secondary analysis of a randomized clinical trial that tested the effects of intraoperative caffeine on postoperative recovery in adults undergoing major surgery. In this substudy, whole-scalp (16-channel) electroencephalographic data were analyzed from a subset of trial participants (n = 55) to determine whether surrogate measures of neural criticality – (1) autocorrelation function of global alpha oscillations and (2) topography of phase relationships via phase lag entropy – were associated with delirium. These measures were analyzed in participants experiencing delirium in the postanesthesia care unit (compared to those without delirium) and in participants randomized to caffeine compared to placebo. Results demonstrated that autocorrelation function in the alpha band was significantly reduced in delirious participants, which is important given that alpha rhythms are postulated to play a vital role in consciousness. Moreover, participants randomized to caffeine demonstrated increased alpha autocorrelation function concurrent with reduced delirium incidence. Lastly, the anterior-posterior topography of phase relationships appeared most preserved in non-delirious participants and in those receiving caffeine. These data suggest that early postoperative delirium may reflect deviations from neural criticality, and caffeine may reduce delirium risk by shifting cortical dynamics toward criticality.

Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy.

Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.

Design and Implementation of Prototype for Portable and Wireless EEG Device

Electroencephalography (EEG) measurement system is used in the health field for the diagnosis of some diseases, and the improvement of the learning process. In this study, an embedded system consisting of two parts, hardware and software has been designed and implemented for processing and displaying the EEG signal. In the hardware section, electronic circuit design and implementation are provided for measuring, amplifying, filtering, extracting EEG rhythms, and wirelessly transmitting the EEG signal. In the software section, a smartphone application interface is developed for that the measured raw EEG signal and rhythms can be displayed in real-time.

Comment on “Physics at the Molecular and Cellular Level (P@MCL): A New Curriculum for Introductory Physics”

I recently became acquainted with the work done by Lisa J. Lapidus on new curricular development for courses on Introductory Physics for the Life Sciences (IPLS) (1). While I enjoyed reading the article, I was expecting a description of the assessment modalities of such a physics course that is highly focused on molecular and cellular biology. The author, however, is still developing this aspect. Several afterthoughts left me wondering about the best assessment modes for this course.From my perspective, introductory physics remains the only basic course in the life sciences curriculum where students are taught to apply logic and deduction to the resolution of real-world physics problems, and this is in striking contrast to molecular and cellular biology, where memorization is traditionally emphasized. I agree with the many voices that argue that the learning objectives of IPLS are not about gaining new knowledge but, rather, are about gaining abilities and competencies. The assessments discussed in the paper refer to concept inventories, which are indeed conceptual rather than problem based. However, in my opinion, written problem-based exams are better suited to evaluate competencies acquired in introductory physics courses. During their final exam, students may be given a list of all the formulas they need, but it will not help them succeed if they did not practice beforehand how to apply this knowledge thoughtfully. Clearly, students are strongly motivated by real-world physics problems that touch upon some biomedical aspects, but when it comes to developing physics problems solely with molecular and cellular biology content, I do not see too many possibilities of constructions that would require application of logic and deduction. This situation implies a high probability that the teacher will have to recycle exam questions and, accordingly, disfavors the problem based assessment modality for P@MCL. In other words, the use of problem-based assessment, along with these curriculum adjustments, would “throw the baby out with the bathwater,” because students would simply train in solving a limited number of typical problems. Probably the optimal assessment mode in such a course would be project based (2). This way, the students have several possibilities to showcase their understanding of physics topics and their competencies to tie together physics and biology—in the form of written essays or video capsules. The evaluation of project-based work, however, may require significant effort on the part of the instructor (3).With regards to curricular adjustments for P@MCL, the elimination of topics from the IPLS curriculum should not be simply based, in my opinion, on whether there is or is not a biological equivalent. Still, I was surprised to see the suppression of rotations, electromagnetic induction, and DC circuits (including Kirchhoff's laws). In molecular biology we have a perfect example of conversion of rotational work into the universal cell energy currency, ATP, by ATP synthase. Electroencephalographic measurements are based on electromagnetic induction caused by pulsating currents in nerve fibers. Finally, although it is clear that Kirchhoff's laws are not required to understand how transmembrane potential is generated on the cellular and molecular levels, it should be noted that the equivalent resistor-capacitor circuits describe quite accurately the electric behavior dynamics of various living cells. After all, this is the great achievement for which Hodgkin and Huxley were awarded their Nobel Prize.

Shining new light on dementia treatment: ADAS‐Cog and quantitative EEG evidence of cognitive improvement in early and mid‐stage dementia using self‐administered 1070 nm transcranial near‐infrared photobiomodulation

AbstractBackgroundThe current evidence supporting nonpharmacologic treatment of dementia has focused mainly on environmental or interactive interventions, e.g., music, sensory stimulation, simulated presence, and validation therapies. Exercise and light therapy improved/maintained activities of daily living, while cognitive stimulation, exercise and reminiscence improved cognition. The strongest evidence for reducing emotional disorders so far has employed music, psychological interventions and reminiscence. PubMed lists 918 citations related to non‐pharmacological treatments for dementia with music therapy and behavioral management techniques being the most efficacious showing modest improvements at best. Non‐pharmacologic treatment interventions directed at reversing both cognitive and behavioral symptom of dementia are small, few in number and with wide variation in defining outcome measures, and little comparative data across approaches to guide further research efforts or systematic clinical applicability. Photobiomodulation using brief, daily, pulsed LED‐driven transcranial infrared light stimulation, has been shown to markedly alter both cognitive and behavioral symptoms in people with mild to moderate dementia. Improved functioning has been documented by both standard neuropsychological testing and noninvasive Quantitative electroencephalographic (QEEG) measures of functional connectivity. This presentation describes a multi‐site, randomized, placebo controlled, at‐home, self‐administered treatment protocol employing pulsed transcranial and intraocular 1068nm near‐infrared light to achieve reversal of cognitive decline, and improve dementia patient’s physical and interpersonal behavior. Study subjects (n=100) were evaluated at two locations at baseline, midpoint (30 days) and termination (60 days) using both Quantitative EEG and ADAS‐Cog and caregiver evaluation of subject’s change in ADLs. This trial was a replication of an earlier pilot trial and a recent safety/feasibility trial with no adverse events reported in any study. MMSE improved 21.0%, 4.8 points in the active treatment group and 1.6 points in the placebo group. Sleep duration increased in the active treatment group by an hour after 8 days of treatment and caregivers noted improved mood, energy, and positive engagement in daily activities. None of these functional changes were noted in the placebo arm subjects. Active group’s clock drawing improved 20% and no change in the control group. Logical memory recall improved 3.3 points (p&lt; .03) and time to completion of Trails A&amp;B decreased 23.7% (p&lt;.03).

Neural computations in children\u2019s third-party interventions are modulated by their parents\u2019 moral values

One means by which humans maintain social cooperation is through intervention in third-party transgressions, a behaviour observable from the early years of development. While it has been argued that pre-school age children’s intervention behaviour is driven by normative understandings, there is scepticism regarding this claim. There is also little consensus regarding the underlying mechanisms and motives that initially drive intervention behaviours in pre-school children. To elucidate the neural computations of moral norm violation associated with young children’s intervention into third-party transgression, forty-seven preschoolers (average age 53.92 months) participated in a study comprising of electroencephalographic (EEG) measurements, a live interaction experiment, and a parent survey about moral values. This study provides data indicating that early implicit evaluations, rather than late deliberative processes, are implicated in a child’s spontaneous intervention into third-party harm. Moreover, our findings suggest that parents’ values about justice influence their children’s early neural responses to third-party harm and their overt costly intervention behaviour.

A Structured Approach to Test the Signal Quality of Electroencephalography Measurements During Use of Head-Mounted Displays for Virtual Reality Applications.

Joint applications of virtual reality (VR) systems and electroencephalography (EEG) offer numerous new possibilities ranging from behavioral science to therapy. VR systems allow for highly controlled experimental environments, while EEG offers a non-invasive window to brain activity with a millisecond-ranged temporal resolution. However, EEG measurements are highly susceptible to electromagnetic (EM) noise and the influence of EM noise of head-mounted-displays (HMDs) on EEG signal quality has not been conclusively investigated. In this paper, we propose a structured approach to test HMDs for EM noise potentially harmful to EEG measures. The approach verifies the impact of HMDs on the frequency- and time-domain of the EEG signal recorded in healthy subjects. The verification task includes a comparison of conditions with and without an HMD during (i) an eyes-open vs. eyes-closed task, and (ii) with respect to the sensory- evoked brain activity. The approach is developed and tested to derive potential effects of two commercial HMDs, the Oculus Rift and the HTC Vive Pro, on the quality of 64-channel EEG measurements. The results show that the HMDs consistently introduce artifacts, especially at the line hum of 50 Hz and the HMD refresh rate of 90 Hz, respectively, and their harmonics. The frequency range that is typically most important in non-invasive EEG research and applications (<50 Hz) however, remained largely unaffected. Hence, our findings demonstrate that high-quality EEG recordings, at least in the frequency range up to 50 Hz, can be obtained with the two tested HMDs. However, the number of commercially available HMDs is constantly rising. We strongly suggest to thoroughly test such devices upfront since each HMD will most likely have its own EM footprint and this article provides a structured approach to implement such tests with arbitrary devices.

Advanced Alarm Method Based on Driver’s State in Autonomous Vehicles

In autonomous driving vehicles, the driver can engage in non-driving-related tasks and does not have to pay attention to the driving conditions or engage in manual driving. If an unexpected situation arises that the autonomous vehicle cannot manage, then the vehicle should notify and help the driver to prepare themselves for retaking manual control of the vehicle. Several effective notification methods based on multimodal warning systems have been reported. In this paper, we propose an advanced method that employs alarms for specific conditions by analyzing the differences in the driver’s responses, based on their specific situation, to trigger visual and auditory alarms in autonomous vehicles. Using a driving simulation, we carried out human-in-the-loop experiments that included a total of 38 drivers and 2 scenarios (namely drowsiness and distraction scenarios), each of which included a control-switching stage for implementing an alarm during autonomous driving. Reaction time, gaze indicator, and questionnaire data were collected, and electroencephalography measurements were performed to verify the drowsiness. Based on the experimental results, the drivers exhibited a high alertness to the auditory alarms in both the drowsy and distracted conditions, and the change in the gaze indicator was higher in the distraction condition. The results of this study show that there was a distinct difference between the driver’s response to the alarms signaled in the drowsy and distracted conditions. Accordingly, we propose an advanced notification method and future goals for further investigation on vehicle alarms.

Evaluation of a Fast Test Based on Biometric Signals to Assess Mental Fatigue at the Workplace-A Pilot Study.

Non-pathological mental fatigue is a recurring, but undesirable condition among people in the fields of office work, industry, and education. This type of mental fatigue can often lead to negative outcomes, such as performance reduction and cognitive impairment in education; loss of focus and burnout syndrome in office work; and accidents leading to injuries or death in the transportation and manufacturing industries. Reliable mental fatigue assessment tools are promising in the improvement of performance, mental health and safety of students and workers, and at the same time, in the reduction of risks, accidents and the associated economic loss (e.g., medical fees and equipment reparations). The analysis of biometric (brain, cardiac, skin conductance) signals has proven to be effective in discerning different stages of mental fatigue; however, many of the reported studies in the literature involve the use of long fatigue-inducing tests and subject-specific models in their methodologies. Recent trends in the modeling of mental fatigue suggest the usage of non subject-specific (general) classifiers and a time reduction of calibration procedures and experimental setups. In this study, the evaluation of a fast and short-calibration mental fatigue assessment tool based on biometric signals and inter-subject modeling, using multiple linear regression, is presented. The proposed tool does not require fatigue-inducing tests, which allows fast setup and implementation. Electroencephalography, photopletismography, electrodermal activity, and skin temperature from 17 subjects were recorded, using an OpenBCI helmet and an Empatica E4 wristband. Correlations to self-reported mental fatigue levels (using the fatigue assessment scale) were calculated to find the best mental fatigue predictors. Three-class mental fatigue models were evaluated, and the best model obtained an accuracy of 88% using three features, (C3), and the (O2 and C3) ratios, from one minute of electroencephalography measurements. The results from this pilot study show the feasibility and potential of short-calibration procedures and inter-subject classifiers in mental fatigue modeling, and will contribute to the use of wearable devices for the development of tools oriented to the well-being of workers and students, and also in daily living activities.