EEG delta activity: an indicator of attention to internal processing during performance of mental tasks

Relationship between preinduction electroencephalogram patterns and propofol sensitivity in adult patients

PURPOSE

To determine the precise induction dose, an objective assessment of individual propofol sensitivity is necessary. This study aimed to investigate whether preinduction electroencephalogram (EEG) data are useful in determining the optimal propofol dose for the induction of general anesthesia in healthy adult patients.

METHODS

Seventy healthy adult patients underwent total intravenous anesthesia (TIVA), and the effect-site target concentration of propofol was observed to measure each individual's propofol requirements for loss of responsiveness. We analyzed preinduction EEG data to assess its relationship with propofol requirements and conducted multiple regression analyses considering various patient-related factors.

RESULTS

Patients with higher relative delta power (ρ = 0.47, p < 0.01) and higher absolute delta power (ρ = 0.34, p = 0.01) required a greater amount of propofol for anesthesia induction. In contrast, patients with higher relative beta power (ρ = -0.33, p < 0.01) required less propofol to achieve unresponsiveness. Multiple regression analysis revealed an independent association between relative delta power and propofol requirements.

CONCLUSION

Preinduction EEG, particularly relative delta power, is associated with propofol requirements during the induction of general anesthesia. The utilization of preinduction EEG data may improve the precision of induction dose selection for individuals.

Firefighter uncompensable heat stress results in excessive upper body temperatures measured by infrared thermography: Implications for cooling strategies

This research sought to evaluate the thermal zones of the upper body and firefighter personal protective equipment (PPE) immediately following uncompensable heat stress (0.03 °C increase/min). We hypothesized that the frontal portion of the head and the inside of the firefighter helmet would be the hottest as measured by infrared thermography. This hypothesis was due to previous research demonstrating that the head accounts for ∼8-10% of the body surface area, but it accounts for ∼20% of the overall body heat dissipation during moderate exercise. Twenty participants performed a 21-min graded treadmill exercise protocol (Altered Modified Naughton) in an environmental chamber (35 °C, 50 % humidity) in firefighter PPE. The body areas analyzed were the frontal area of the head, chest, abdomen, arm, neck, upper back, and lower back. The areas of the PPE that were analyzed were the inside of the helmet and the jacket. The hottest areas of the body post-exercise were the frontal area of the head (mean: 37.3 ± 0.4 °C), chest (mean: 37.5 ± 0.3 °C), and upper back (mean: 37.3 ± 0.4 °C). The coldest area of the upper body was the abdomen (mean: 36.1 ± 0.4 °C). The peak temperature of the inside of the helmet increased (p < 0.001) by 9.8 °C from 27.7 ± 1.6 °C to 37.4 ± 0.7 °C, and the inside of the jacket increased (p < 0.001) by 7.3 °C from 29.2 ± 1.7 °C to 36.5 ± 0.4 °C. The results of this study are relevant for cooling strategies for firefighters.

EEG and fNIRS are associated with situation awareness (hazard) prediction during a driving task

Situation awareness (SA) is important in many demanding tasks (e.g. driving). Assessing SA during training can indicate whether someone is ready to perform in the real world. SA is typically assessed by interrupting the task to ask questions about the situation or asking questions after task completion, assessing only momentary SA. An objective and continuous means of detecting SA is needed. We examined whether neurophysiological sensors are useful to objectively measure Level 3 SA (projection of events into the future) during a driving task. We measured SA by the speed at which participants responded to SA questions and the accuracy of responses. For EEG, beta and theta power were most sensitive to SA response time. For fNIRS, oxygenated haemoglobin (HbO) was most sensitive to accuracy. This is the first evidence to our knowledge that neurophysiological measures are useful for assessing Level 3 SA during an ecologically valid task.

Age-Related Aspects of Sex Differences in Event-Related Brain Oscillatory Responses: A Turkish Study

Earlier research has suggested gender differences in event-related potentials/oscillations (ERPs/EROs). Yet, the alteration in event-related oscillations (EROs) in the delta and theta frequency bands have not been explored between genders across the three age groups of adulthood, i.e., 18-50, 51-65, and >65 years. Data from 155 healthy elderly participants who underwent a neurological examination, comprehensive neuropsychological assessment (including attention, memory, executive function, language, and visuospatial skills), and magnetic resonance imaging (MRI) from past studies were used. The delta and theta ERO powers across the age groups and between genders were compared and correlational analyses among the ERO power, age, and neuropsychological tests were performed. The results indicated that females displayed higher theta ERO responses than males in the frontal, central, and parietal regions but not in the occipital location between 18 and 50 years of adulthood. The declining theta power of EROs in women reached that of men after the age of 50 while the theta ERO power was more stable across the age groups in men. Our results imply that the cohorts must be recruited at specified age ranges across genders, and clinical trials using neurophysiological biomarkers as an intervention endpoint should take gender into account in the future.

Inter-brain entrainment (IBE) during interoception. A multimodal EEG-fNIRS coherence-based hyperscanning approach

This work examined the impact of interoceptive manipulation and the presence of a shared goal on inter-brain entrainment (IBE) during a motor synchronization task. A multimodal functional Near Infrared Spectroscopy - Electroencephalogram (fNIRS-EEG) system-based hyperscanning approach was applied to 13 dyads performing the motor synchrony task during an interoceptive (focus on the breath) and control condition. Additionally, two version of the motor task-one with and one without a clearly defined common goal-were presented to participants to emphasize the task's collaborative purpose. The multimodal approach was exploited to record the electrophysiological (EEG) cortical oscillation and hemodynamic (oxy-Hb and deoxy-Hb) levels. Results revealed significant correlations between EEG delta, theta, and alpha band and hemodynamic oxy-Hb in the left compared to right hemisphere for the interoceptive confronted with the control condition. This significant EEG/fNIRS IBE correlation was also found for delta and theta band whereas the task was presented with an explicit shared goal confronted with the no-social version. In addition to separate functional connectivity EEG and fNIRS analysis, this study proposed a novel analysis pipeline including statistical tests for examining the coherence between functional connectivity EEG-fNIRS signals within couples. Besides proposing methodological advancements on EEG-fNIRS signals hyperscanning analysis, this research demonstrated that, in dyads undertaking a motor synchronization task, both the interoceptive attention to respiration and an explicit joint intention activate left anterior regions.

Spatio-Temporal Image-Based Encoded Atlases for EEG Emotion Recognition

Emotion recognition plays an essential role in human-human interaction since it is a key to understanding the emotional states and reactions of human beings when they are subject to events and engagements in everyday life. Moving towards human-computer interaction, the study of emotions becomes fundamental because it is at the basis of the design of advanced systems to support a broad spectrum of application areas, including forensic, rehabilitative, educational, and many others. An effective method for discriminating emotions is based on ElectroEncephaloGraphy (EEG) data analysis, which is used as input for classification systems. Collecting brain signals on several channels and for a wide range of emotions produces cumbersome datasets that are hard to manage, transmit, and use in varied applications. In this context, the paper introduces the Empátheia system, which explores a different EEG representation by encoding EEG signals into images prior to their classification. In particular, the proposed system extracts spatio-temporal image encodings, or atlases, from EEG data through the Processing and transfeR of Interaction States and Mappings through Image-based eNcoding (PRISMIN) framework, thus obtaining a compact representation of the input signals. The atlases are then classified through the Empátheia architecture, which comprises branches based on convolutional, recurrent, and transformer models designed and tuned to capture the spatial and temporal aspects of emotions. Extensive experiments were conducted on the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED) public dataset, where the proposed system significantly reduced its size while retaining high performance. The results obtained highlight the effectiveness of the proposed approach and suggest new avenues for data representation in emotion recognition from EEG signals.

Lower oddball event-related EEG delta and theta responses in patients with dementia due to Parkinson's and Lewy body than Alzheimer's disease

Oddball task-related EEG delta and theta responses are associated with frontal executive functions, which are significantly impaired in patients with dementia due to Parkinson's disease (PDD) and Lewy bodies (DLB). The present study investigated the oddball task-related EEG delta and theta responses in patients with PDD, DLB, and Alzheimer's disease dementia (ADD). During visual and auditory oddball paradigms, EEG activity was recorded in 20 ADD, 17 DLB, 20 PDD, and 20 healthy (HC) older adults. Event-related EEG power spectrum and phase-locking analysis were performed at the delta (1-4 Hz) and theta (4-7 Hz) frequency bands for target and nontarget stimuli. Compared to the HC persons, dementia groups showed lower frontal and central delta and theta power and phase-locking associated with task performance and neuropsychological test scores. Notably, this effect was more significant in the PDD and DLB than in the ADD. In conclusion, oddball task-related frontal and central EEG delta and theta responses may reflect frontal supramodal executive dysfunctions in PDD and DLB patients.

A Subject-Specific Attention Index Based on the Weighted Spectral Power

As an essential cognitive function, attention has been widely studied and various indices based on EEG have been proposed for its convenience and easy availability for real-time attention monitoring. Although existing indices based on spectral power of empirical frequency bands are able to describe the attentional state in some way, the reliability still needs to be improved. This paper proposed a subject-specific attention index based on the weighted spectral power. Unlike traditional indices, the ranges of frequency bands are not empirical but obtained from subject-specific change patterns of spectral power of electroencephalograph (EEG) to overcome the great inter-subject variance. In addition, the contribution of each frequency component in the frequency band is considered different. Specifically, the ratio of power spectral density (PSD) function in attentional and inattentional state is utilized to calculate the weight to enhance the effectiveness of the proposed index. The proposed subject-specific attention index based on the weighted spectral power is evaluated on two open datasets including EEG data of a total of 44 subjects. The results of the proposed index are compared with 3 traditional attention indices using various statistical analysis methods including significance tests and distribution variance measurements. According to the experimental results, the proposed index can describe the attentional state more accurately. The proposed index respectively achieves accuracies of 86.21% and 70.00% at the 1% significance level in both the t-test and Wilcoxon rank-sum test for two datasets, which obtains improvements of 41.38% and 20.00% compared to the best result of the traditional indices. These results indicate that the proposed index provides an efficient way to measure attentional state.

The fear of COVID-19 contagion: an exploratory EEG-fMRI study

Pandemics have the potential to change how people behave and feel. The COVID-19 pandemic is no exception; thus, it may serve as a "challenging context" for understanding how pandemics affect people's minds. In this study, we used high-density electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to examine the neural correlates of fear of contagion during the most critical moments of COVID-19 in Italy (i.e., October 2020-May 2021). To do that, we stimulated participants (N = 17; nine females) with artificial-intelligence-generated faces of people presented as healthy, recovered from COVID-19, or infected by SARS-CoV-2. The fMRI results documented a modulation of large bilateral fronto-temporo-parietal functional brain networks. Critically, we found selective recruitment of cortical (e.g., frontal lobes) and subcortical fear-related structures (e.g., amygdala and putamen) of the so-called social brain network when participants observed COVID-19-related faces. Consistently, EEG results showed distinct patterns of brain activity selectively associated with infected and recovered faces (e.g., delta and gamma rhythm). Together, these results highlight how pandemic contexts may reverberate in the human brain, thus influencing most basic social and cognitive functioning. This may explain the emergence of a cluster of psychopathologies during and after the COVID-19 pandemic. Therefore, this study underscores the need for prompt interventions to address pandemics' short- and long-term consequences on mental health.

Effort expenditure modulates feedback evaluations involving self-other agreement: evidence from brain potentials and neural oscillations

The influence of effort expenditure on the subjective value in feedback involving material reward has been the focus of previous research. However, little is known about the impact of effort expenditure on subjective value evaluations when feedback involves reward that is produced in the context of social interaction (e.g. self-other agreement). Moreover, how effort expenditure influences confidence (second-order subjective value) in feedback evaluations remains unclear. Using electroencephalography, this study aimed to address these questions. Event-related potentials showed that, after exerting high effort, participants exhibited increased reward positivity difference in response to self-other (dis)agreement feedback. After exerting low effort, participants reported high confidence, and the self-other disagreement feedback evoked a larger P3a. Time-frequency analysis showed that the high-effort task evoked increased frontal midline theta power. In the low (vs. high)-effort task, the frontal midline delta power for self-other disagreement feedback was enhanced. These findings suggest that, at the early feedback evaluation stage, after exerting high effort, individuals exhibit an increased sensitivity of subjective value evaluation in response to self-other agreement feedback. At the later feedback evaluation stage, after completing the low-effort task, the self-other disagreement feedback violates the individuals'high confidence and leads to a metacognitive mismatch.

Recognizing the situation awareness of forklift operators based on EEG techniques in a field experiment

Lack of situation awareness (SA) is the primary cause of human errors when operating forklifts, so determining the SA level of the forklift operator is crucial to the safety of forklift operations. An EEG recognition approach of forklift operator SA in actual settings was presented in order to address the issues with invasiveness, subjectivity, and intermittency of existing measuring methods. In this paper, we conducted a field experiment that mimicked a typical forklift operation scenario to verify the differences in EEG states of forklift operators with different SA levels and investigate the correlation of multi-band combination features of each brain region of forklift operators with SA. Based on the sensitive EEG combination indexes, Support Vector Mechanism was used to construct a forklift operator SA recognition model. The results revealed that there were differences between forklift operators with high and low SA in the θ, α, and β frequency bands in zones F, C, P, and O; combined EEG indicators θ/β, (α + θ)/(α + β), and θ/(α + β) in zones F, P, and C were significantly correlated with SA; the recognition accuracy of the model reached 88.64% in the case of combined EEG indicators of zones C & F & P as input. It could provide a reference for SA measurement, contributing to the improvement of SA.

Electroencephalographic activity during direct breastfeeding and breast milk expression in primiparous mothers

Breastfeeding is recognized worldwide as the best option for infant feeding. Expressing breast milk is an alternative for mothers to provide their infants all the benefits of maternal milk. During breast milk expression, mothers receive a distinct kind of sensory stimulation, because there is no direct bodily or affective interaction with their infants, many women report feeling isolated, generating a love-hate relation with pumping, and even low levels of satisfaction while expressing breast milk. While it is well known that the prefrontal, parietal, and temporal cortices play important roles in the emotional and cognitive processing of maternal stimuli, knowledge about how these cortical areas function during breastfeeding is lacking. This study was designed to characterize EEG activity in the prefrontal and parietal cortices and the affective scores of primiparous breastfeeding mothers during two conditions of milk expression: breast milk expression and direct breastfeeding. Participants reported higher valence and arousal and a pleasant state during direct breastfeeding. In the direct breastfeeding condition, both prefrontal areas showed a higher absolute power (AP) of the slow bands, with a lower AP of the alpha band in the parietal cortex. A lower correlation between frontopolar and dorsolateral areas with a higher correlation between prefrontal and parietal cortices was obtained mainly in the right hemisphere. This EEG activity could be linked to an internal state of focused attention and, simultaneously, open monitoring of the environment that suggests an integration of the motive-emotional and cognitive processes necessary for adequate mother-baby interaction during direct breastfeeding.

Math on cortex-enhanced delta phase synchrony in math experts during long and complex math demonstrations

Neural oscillations are important for working memory and reasoning and they are modulated during cognitively challenging tasks, like mathematics. Previous work has examined local cortical synchrony on theta (4-8 Hz) and alpha (8-13 Hz) bands over frontal and parietal electrodes during short mathematical tasks when sitting. However, it is unknown whether processing of long and complex math stimuli evokes inter-regional functional connectivity. We recorded cortical activity with EEG while math experts and novices watched long (13-68 seconds) and complex (bachelor-level) math demonstrations when sitting and standing. Fronto-parietal connectivity over the left hemisphere was stronger in math experts than novices reflected by enhanced delta (0.5-4 Hz) phase synchrony in experts. Processing of complex math tasks when standing extended the difference to right hemisphere, suggesting that other cognitive processes, such as maintenance of body balance when standing, may interfere with novice's internal concentration required during complex math tasks more than in experts. There were no groups differences in phase synchrony over theta or alpha frequencies. These results suggest that low-frequency oscillations modulate inter-regional connectivity during long and complex mathematical cognition and demonstrate one way in which the brain functions of math experts differ from those of novices: through enhanced fronto-parietal functional connectivity.

Investigating mental workload caused by NDRTs in highly automated driving with deep learning

OBJECTIVE

This study aimed to examine the impact of non-driving-related tasks (NDRTs) on drivers in highly automated driving scenarios and sought to develop a deep learning model for classifying mental workload using electroencephalography (EEG) signals.

METHODS

The experiment involved recruiting 28 participants who engaged in simulations within a driving simulator while exposed to 4 distinct NDRTs: (1) reading, (2) listening to radio news, (3) watching videos, and (4) texting. EEG data collected during NDRTs were categorized into 3 levels of mental workload, high, medium, and low, based on the NASA Task Load Index (NASA-TLX) scores. Two deep learning methods, namely, long short-term memory (LSTM) and bidirectional long short-term memory (BLSTM), were employed to develop the classification model.

RESULTS

A series of correlation analyses revealed that the channels and frequency bands are linearly correlated with mental workload. The comparative analysis of classification results demonstrates that EEG data featuring significantly correlated frequency bands exhibit superior classification accuracy compared to the raw EEG data.

CONCLUSIONS

This research offers a reference for assessing mental workload resulting from NDRTs in the context of highly automated driving. Additionally, it delves into the development of deep learning classifiers for EEG signals with heightened accuracy.

Evaluation of Brain Electrical Activity of Visual Working Memory with Time-Frequency Analysis

The term visual working memory (VWM) refers to the temporary storage of visual information. In electrophysiological recordings during the change detection task which relates to VWM, contralateral negative slow activity was detected. It was found to occur during the information is kept in memory and it was called contralateral delay activity. In this study, the characteristics of electroencephalogram frequencies of the contralateral and ipsilateral responses in the retention phase of VWM were evaluated by using time-frequency analysis (discrete wavelet transform [DWT]) in the change detection task. Twenty-six volunteers participated in the study. Event-related brain potentials (ERPs) were examined, and then a time-frequency analysis was performed. A statistically significant difference between contralateral and ipsilateral responses was found in the ERP. DWT showed a statistically significant difference between contralateral and ipsilateral responses in the delta and theta frequency bands range. When volunteers were grouped as either high or low VWM capacity the time-frequency analysis between these groups revealed that high memory capacity groups have a significantly higher negative coefficient in alpha and beta frequency bands. This study showed that during the retention phase delta and theta bands may relate to visual memory retention and alpha and beta bands may reflect individual memory capacity.

Predicting the subjective intensity of imagined experiences from electrophysiological measures of oscillatory brain activity

Most people can conjure images and sounds that they experience in their minds. There are, however, marked individual differences. Some people report that they cannot generate imagined sensory experiences at all (aphantasics) and others report that they have unusually intense imagined experiences (hyper-phantasics). These individual differences have been linked to activity in sensory brain regions, driven by feedback. We would therefore expect imagined experiences to be associated with specific frequencies of oscillatory brain activity, as these can be a hallmark of neural interactions within and across regions of the brain. Replicating a number of other studies, relative to a Resting-State we find that the act of engaging in auditory or in visual imagery is linked to reductions in the power of oscillatory brain activity across a broad range of frequencies, with prominent peaks in the alpha band (8-12 Hz). This oscillatory activity, however, did not predict individual differences in the subjective intensity of imagined experiences. For audio imagery, these were rather predicted by reductions within the theta (6-9 Hz) and gamma (33-38 Hz) bands, and by increases in beta (15-17 Hz) band activity. For visual imagery these were predicted by reductions in lower (14-16 Hz) and upper (29-32 Hz) beta band activity, and by an increase in mid-beta band (24-26 Hz) activity. Our data suggest that there is sufficient ground truth in the subjective reports people use to describe the intensity of their imagined sensory experiences to allow these to be linked to the power of distinct rhythms of brain activity. In future, we hope to combine this approach with better measures of the subjective intensity of imagined sensory experiences to provide a clearer picture of individual differences in the subjective intensity of imagined experiences, and of why these eventuate.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.

Oscillatory Responses to Tactile Stimuli of Different Intensity

Tactile perception encompasses several submodalities that are realized with distinct sensory subsystems. The processing of those submodalities and their interactions remains understudied. We developed a paradigm consisting of three types of touch tuned in terms of their force and velocity for different submodalities: discriminative touch (haptics), affective touch (C-tactile touch), and knismesis (alerting tickle). Touch was delivered with a high-precision robotic rotary touch stimulation device. A total of 39 healthy individuals participated in the study. EEG cluster analysis revealed a decrease in alpha and beta range (mu-rhythm) as well as theta and delta increase most pronounced to the most salient and fastest type of stimulation. The participants confirmed that slower stimuli targeted to affective touch low-threshold receptors were the most pleasant ones, and less intense stimuli aimed at knismesis were indeed the most ticklish ones, but those sensations did not form an EEG cluster, probably implying their processing involves deeper brain structures that are less accessible with EEG.

Methylliberine Ingestion Improves Various Indices of Affect but Not Cognitive Function in Healthy Men and Women

This study assessed the acute effects of oral methylliberine (DynamineTM) supplementation on cognitive function and indices of well-being. This was a double-blind, randomized, within-subject crossover trial. In total, 25 healthy men and women (33.5 ± 10.7 yr, 172.7 ± 8.6 cm, 73.3 ± 11.0 kg) underwent pretesting before ingesting methylliberine (100 mg) or a placebo (PLA) for 3 days. On the fourth day, the participants were tested before their fourth dose (baseline) and every hour post-ingestion for 3 h. After a one-week washout period, the participants repeated testing with the alternate investigational product. The testing battery consisted of vitals, Stroop test, Trail Making Test-B, and visual analog scales that assessed various indices of well-being. Mixed factorial ANOVAs with repeated measures were used to assess all variables. There were significant (p ≤ 0.050) interactions in terms of concentration, motivation, and mood. Methylliberine improved concentration at 1 and 3 h, motivation at 3 h, and mood at 1, 2, and 3 h (p ≤ 0.050). Methylliberine improved energy, sustained energy, and mood in all participants to a greater extent than PLA at 1 h and 3 h relative to baseline (p ≤ 0.050). PLA improved motivation at 1 and 2 h and mood at 2 h (p ≤ 0.050). Methylliberine improved concentration, well-being, and the ability to tolerate stress to a greater extent than PLA at 3 h relative to baseline (p ≤ 0.050). Women observed elevations in sustained energy at 1 and 3 h (p ≤ 0.050) with methylliberine vs. PLA. Methylliberine had a negligible influence on cognitive function and vitals (p > 0.050), and no adverse events were reported. Methylliberine significantly improved subjective feelings of energy, concentration, motivation, and mood, but not cognitive function. PLA improved motivation and mood at hours 1 and 2, while methylliberine sustained these benefits for longer. Methylliberine also improved concentration, well-being, and the ability to tolerate stress to a greater degree than PLA, while having no detrimental effects on vital signs. Methylliberine also seemed to have a positive impact on sustained energy in women.

The role of mindfulness on theta inter-brain synchrony during cooperation feedback processing: An EEG-based hyperscanning study

Mindfulness appears to improve empathy and understanding in relationships, which are necessary for successful cooperation. However, the impact of mindfulness on cooperation has not been fully studied. This study used hyperscanning technique to examine the effect of mindfulness on the inter-brain synchrony of interacting individuals during the cooperative tasks. Forty-one dyads were randomly assigned to a mindfulness group or a non-mindfulness group. Dyads of the mindfulness group performed a short mindfulness exercise following a 15-minute mindfulness audio guidance. Dyads of the non-mindfulness group were instructed to rest quietly with their eyes closed. Then, simultaneously and continuously EEG was recorded from all dyads when they completed a computer-based cooperative game task. Reaction times (RTs) and success rates were used to indicate the behavioral performance, and phase locking value (PLV) was used to indicate the inter-brain synchrony. The results showed that (1) Greater theta inter-brain synchrony during the cooperative computer game tasks was observed in the mindfulness group than in the non-mindfulness group; (2) Greater theta inter-brain synchrony was observed in the successful cooperation conditions as compared to those in the failure cooperation conditions; (3) Greater theta inter-brain synchrony was observed at the frontal region as compared to those at the parietal-occipital region in the successful cooperation condition. The results expand the neural basis of the effects of mindfulness on cooperation feedback processing.

Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems

Introduction

As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.

Methods

In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).

Results and discussion

Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.

Altered Brain Connectivity and Network Topological Organization in a Non-ordinary State of Consciousness Induced by Hypnosis

Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration-segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis.

Long-term resting EEG correlates of repetitive mild traumatic brain injury and loss of consciousness: alterations in alpha-beta power

Objective

Long-term changes to EEG spectra after mild traumatic brain injury (mTBI, i.e., concussion) have been reported; however, the role of injury characteristics in long-term EEG changes is unclear. It is also unclear how any chronic EEG changes may underlie either subjective or objective cognitive difficulties, which might help explain the variability in recovery after mTBI.

Methods

This study included resting-state high-density electroencephalography (EEG) and mTBI injury data from 340 service members and veterans collected on average 11 years after injury as well as measures of objective and subjective cognitive functioning. The average absolute power within standard bands was computed across 11 spatial regions of the scalp. To determine how variation in brain function was accounted for by injury characteristics and aspects of cognition, we used regression analyses to investigate how EEG power was predicted by mTBI history characteristics [number, number with post-traumatic amnesia and witnessed loss of consciousness (PTA + LOC), context of injury (combat or non-combat), potentially concussive blast exposures], subjective complaints (TBIQOL General Cognitive and Executive Function Concerns), and cognitive performance (NIH Toolbox Fluid Intelligence and premorbid IQ).

Results

Post-traumatic amnesia (PTA) and loss of consciousness (LOC), poorer cognitive performance, and combat experience were associated with reduced power in beta frequencies. Executive function complaints, lower premorbid IQ, poorer cognitive performance, and higher psychological distress symptoms were associated with greater power of delta frequencies. Multiple regression confirmed the relationship between PTA + LOC, poor cognitive performance, cognitive complaints, and reduced power in beta frequencies and revealed that repetitive mTBI was associated with a higher power in alpha and beta frequencies. By contrast, neither dichotomous classification of the presence and absence of mTBI history nor blast exposures showed a relationship with EEG power variables.

Conclusion

Long-term alterations in resting EEG spectra measures of brain function do not appear to reflect any lasting effect of a history of mTBI or blast exposures. However, power in higher frequencies reflects both injury characteristics and subjective and objective cognitive difficulties, while power in lower frequencies is related to cognitive functions and psychological distress associated with poor long-term outcomes after mTBI.

Characterizing endogenous delta oscillations in human MEG

Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

A comparative study of the cortical function during the interpretation of algorithms in pseudocode and the solution of first-order algebraic equations

This study intends to determine whether similarities of the functioning of the cerebral cortex exist, modeled as a graph, during the execution of mathematical tasks and programming related tasks. The comparison is done using network parameters and during the development of computer programming tasks and the solution of first-order algebraic equations. For that purpose, electroencephalographic recordings (EEG) were made with a volunteer group of 16 students of systems engineering of Universidad del Norte in Colombia, while they were performing computer programming tasks and solving first-order algebraic equations with three levels of difficulty. Then, based on the Synchronization Likelihood method, graph models of functional cortical networks were developed, whose parameters of Small-Worldness (SWN), global(Eg) and local (El) efficiency were compared between both types of tasks. From this study, it can be highlighted, first, the novelty of studying cortical function during the solution of algebraic equations and during programming tasks; second, significant differences between both types of tasks observed only in the delta and theta bands. Likewise, the differences between simpler mathematical tasks with the other levels in both types of tasks; third, the Brodmann areas 21 and 42, associated with auditory sensory processing, can be considered as differentiating elements of programming tasks; as well as Brodmann area 8, during equation solving.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Measurement of craving among gamers with internet gaming disorder using repeated presentations of game videos: a resting-state electroencephalography study

BACKGROUND

Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function.

METHODS

In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD, n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed.

RESULTS

The IGD group exhibited higher absolute powers of D_C, D_PO, T_C, T_PO, B_C, and B_PO than HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test).

CONCLUSIONS

These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs.

Functional connectivity fingerprints of the frontal eye field and inferior frontal junction suggest spatial versus nonspatial processing in the prefrontal cortex

Neuroimaging evidence suggests that the frontal eye field (FEF) and inferior frontal junction (IFJ) govern the encoding of spatial and nonspatial (such as feature- or object-based) representations, respectively, both during visual attention and working memory tasks. However, it is still unclear whether such contrasting functional segregation is also reflected in their underlying functional connectivity patterns. Here, we hypothesized that FEF has predominant functional coupling with spatiotopically organized regions in the dorsal ('where') visual stream whereas IFJ has predominant functional connectivity with the ventral ('what') visual stream. We applied seed-based functional connectivity analyses to temporally high-resolving resting-state magnetoencephalography (MEG) recordings. We parcellated the brain according to the multimodal Glasser atlas and tested, for various frequency bands, whether the spontaneous activity of each parcel in the ventral and dorsal visual pathway has predominant functional connectivity with FEF or IFJ. The results show that FEF has a robust power correlation with the dorsal visual pathway in beta and gamma bands. In contrast, anterior IFJ (IFJa) has a strong power coupling with the ventral visual stream in delta, beta and gamma oscillations. Moreover, while FEF is phase-coupled with the superior parietal lobe in the beta band, IFJa is phase-coupled with the middle and inferior temporal cortex in delta and gamma oscillations. We argue that these intrinsic connectivity fingerprints are congruent with each brain region's function. Therefore, we conclude that FEF and IFJ have dissociable connectivity patterns that fit their respective functional roles in spatial versus nonspatial top-down attention and working memory control.

Predicting collision cases at unsignalized intersections using EEG metrics and driving simulator platform

Unsignalized intersection collision has been one of the most dangerous accidents in the world. How to identify road hazards and predict the potential intersection collision ahead are challenging problems in traffic safety. This paper studies the feasibility of EEG metrics to forecast road hazards and presents an improved neural network model to predict intersection collision based on EEG metrics and driving behavior. It is demonstrated that EEG metrics show significant differences between collision and non-collision cases. It indicates that EEG metrics can serve as effective indicators to predict the collision probability. The drivers with higher relative power in fast frequency band (alpha and beta), lower relative power in slow frequency band (delta and theta) are more likely to have conflicts. The prediction using three machine learning models (Multi-layer perceptron (MLP), Logistic regression (LR) and Random forest (RF)) based on three input datasets (only EEG metrics, only driving behavior and combined EEG metrics with driving behavior) are compared. The results show that for single time point prediction, MLP model has the highest accuracy among three machine learning models. The model solely based on EEG metrics datasets has higher accuracy than driving behavior as well as combined datasets. However, for multi-time point prediction, the accuracy of MLP is only 73.9%, worse than LR and RF. We improved the MLP model by adding attention mechanism layer and using random forest model to select important features. As a consequence, the accuracy is greatly improved and reaches 88%. This study demonstrates the importance and feasibility of EEG signals to identify unsafe drivers ahead. The improved neural network model can be helpful to reduce intersection accidents and improve traffic safety.

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA.

Effect of the level of task abstraction on the transfer of knowledge from virtual environments in cognitive and motor tasks

Introduction

Virtual environments are increasingly being used for training. It is not fully understood what elements of virtual environments have the most impact and how the virtual training is integrated by the brain on the sought-after skill transference to the real environment. In virtual training, we analyzed how the task level of abstraction modulates the brain activity and the subsequent ability to execute it in the real environment and how this learning generalizes to other tasks. The training of a task under a low level of abstraction should lead to a higher transfer of skills in similar tasks, but the generalization of learning would be compromised, whereas a higher level of abstraction facilitates generalization of learning to different tasks but compromising specific effectiveness.

Methods

A total of 25 participants were trained and subsequently evaluated on a cognitive and a motor task following four training regimes, considering real vs. virtual training and low vs. high task abstraction. Performance scores, cognitive load, and electroencephalography signals were recorded. Transfer of knowledge was assessed by comparing performance scores in the virtual vs. real environment.

Results

The performance to transfer the trained skills showed higher scores in the same task under low abstraction, but the ability to generalize the trained skills was manifested by higher scores under high level of abstraction in agreement with our hypothesis. Spatiotemporal analysis of the electroencephalography revealed higher initial demands of brain resources which decreased as skills were acquired.

Discussion

Our results suggest that task abstraction during virtual training influences how skills are assimilated at the brain level and modulates its manifestation at the behavioral level. We expect this research to provide supporting evidence to improve the design of virtual training tasks.

Commonalities between the Berger Rhythm and spectra differences driven by cross-modal attention and imagination

When people close their eyes, the power of alpha-band oscillatory brain activity increases. We explored the possibility that this could be related to a suppression of visual processing, rather than being a default dynamic of the visual brain. We recorded brain activity while people meditated with their eyes open or closed, and when people attended to or imagined having auditory or visual experiences. We could decode the attended or imagined modality of experiences based on the spectra of brain activity that prevailed while meditating with open or closed eyes. We also found anecdotal evidence suggesting the strength of imagined sensory experiences may be predicted by the dynamics of neural networks that are responsive to inputs. Overall, our data suggest spectra changes when people close their eyes might relate to a targeted suppression of visual processing, as opposed to being a default state of idle visual brains.

Machine Learning Based on Event-Related EEG of Sustained Attention Differentiates Adults with Chronic High-Altitude Exposure from Healthy Controls

(1) Objective: The aim of this study was to examine the effect of high altitude on inhibitory control processes that underlie sustained attention in the neural correlates of EEG data, and explore whether the EEG data reflecting inhibitory control contain valuable information to classify high-altitude chronic hypoxia and plain controls. (2) Methods: 35 chronic high-altitude hypoxic adults and 32 matched controls were recruited. They were required to perform the go/no-go sustained attention task (GSAT) using event-related potentials. Three machine learning algorithms, namely a support vector machine (SVM), logistic regression (LR), and a decision tree (DT), were trained based on the related ERP components and neural oscillations to build a dichotomous classification model. (3) Results: Behaviorally, we found that the high altitude (HA) group had lower omission error rates during all observation periods than the low altitude (LA) group. Meanwhile, the ERP results showed that the HA participants had significantly shorter latency than the LAs for sustained potential (SP), indicating vigilance to response-related conflict. Meanwhile, event-related spectral perturbation (ERSP) analysis suggested that lowlander immigrants exposed to high altitudes may have compensatory activated prefrontal cortexes (PFC), as reflected by slow alpha, beta, and theta frequency-band neural oscillations. Finally, the machine learning results showed that the SVM achieved the optimal classification F1 score in the later stage of sustained attention, with an F1 score of 0.93, accuracy of 92.54%, sensitivity of 91.43%, specificity of 93.75%, and area under ROC curve (AUC) of 0.97. The results proved that SVM classification algorithms could be applied to identify chronic high-altitude hypoxia. (4) Conclusions: Compared with other methods, the SVM leads to a good overall performance that increases with the time spent on task, illustrating that the ERPs and neural oscillations may provide neuroelectrophysiological markers for identifying chronic plateau hypoxia.

Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Effects of mindfulness in patients with mild cognitive impairment with insomnia: A double-blind randomized controlled trial

OBJECTIVE

Current research on the effects of mindfulness therapy on MCI and insomnia has been inconsistent. It is still a hot topic of research and discussion. This study aimed to improve the sleep quality, cognition, and mental state of patients with mild cognitive impairment (MCI) with insomnia.

METHODS

A double-blind randomized controlled trial was conducted. Seventy-five patients who met the eligibility criteria were randomly assigned to the mindfulness (n = 38) or health education (n = 37) treatment group. The primary outcomes were sleep, measured by the Pittsburgh Sleep Quality Inventory, and cognition, measured by The Montreal Cognitive Assessment and Mini-Mental State Examination. Secondary outcomes included insomnia, measured by the Insomnia Severity Index, depression, anxiety, and perceived stress. EEG signals were collected at rest with eyes closed in the mindfulness state. The power spectrum was analyzed from these data.

RESULTS

Cognitive function and sleep quality were significantly improved in the mindfulness group (95% confidence interval 0.04 - 0.05, 0.03 - 0.04, -5.58 - -1.55, respectively). Anxiety and perceived stress scores were significantly lower than those in the control group (95% confidence interval 0.002 - 0.004, 0.009 - 0.013, respectively). The power spectrum differences in δ, θ, β, and γ bands were significant between the rest and mindfulness states (P < .05). Good safety was achieved in both groups with no deaths or serious adverse events.

CONCLUSION

Mindfulness improved sleep quality, cognitive function, and mentality of patients. Mindfulness practice caused deep relaxation in the brain and changes in electrical frequency bands associated with attention and cognitive tasks. Mindfulness learning can be performed successfully for individuals with MCI. Additionally, it is suitable for adoption in nursing homes.

EEG temporal-spatial transformer for person identification

An increasing number of studies have been devoted to electroencephalogram (EEG) identity recognition since EEG signals are not easily stolen. Most of the existing studies on EEG person identification have only addressed brain signals in a single state, depending upon specific and repetitive sensory stimuli. However, in reality, human states are diverse and rapidly changing, which limits their practicality in realistic settings. Among many potential solutions, transformer is widely used and achieves an excellent performance in natural language processing, which demonstrates the outstanding ability of the attention mechanism to model temporal signals. In this paper, we propose a transformer-based approach for the EEG person identification task that extracts features in the temporal and spatial domains using a self-attention mechanism. We conduct an extensive study to evaluate the generalization ability of the proposed method among different states. Our method is compared with the most advanced EEG biometrics techniques and the results show that our method reaches state-of-the-art results. Notably, we do not need to extract any features manually.

Cognitive performance and electroencephalographic variations in air traffic controllers under various mental workload and time of day

The aim of this study was to investigate the effects of mental workload (MWL) and time of day on cognitive performance and electroencephalographic (EEG) parameters of air traffic controllers. EEG signals recorded while 20 professional air traffic controllers performed cognitive tasks [A-X Continuous Performance Test (AX-CPT) and 3-back working memory task] after they were exposed to two levels of task difficulty (high and low MWL) in the morning and afternoon. Significant decreases in cognitive performance were found when the levels of task difficulty increased in both tasks. The results confirmed the sensitivity of the theta and beta activities to levels of task difficulty in the 3-back task, while they were not affected in the AX-CPT. Theta and beta activities were influenced by time of day in the AX-CPT. The findings provide guidance for application of changes in EEG parameters when MWL level is manipulated during the day that could be implemented in future for the development of real-time monitoring systems to improve aviation safety.

Functional connectivity changes in the delta frequency band following trauma treatment in complex trauma and dissociative disorder patients

Objective

Phase-oriented trauma treatment is efficacious in the treatment of complex trauma and dissociative disorder patients. However, the neural correlates of this therapeutic effect are not yet well-understood. In the current study we investigated whether patients show a strengthening in functional network connectivity in the delta frequency band (1-3.5 Hz) over the course of phase-oriented inpatient trauma treatment while they performed an emotion regulation task. Further, we examined whether neural changes were associated with symptom reduction and improvement in emotion regulation skills.

Methods

Before and after 8 weeks of treatment, electroencephalography (EEG) was acquired in patients (n = 28) with a complex posttraumatic stress disorder (cPTSD) or complex dissociative disorder (CDD). They also completed clinical and emotion regulation questionnaires. To delimit data variability, patients participated as one dissociative part that is referred to as Apparently Normal Part (ANP). Patients' data were compared to a matched healthy control croup (n = 38), also measured twice.

Results

Prior to treatment, functional connectivity was significantly lower in patients compared to controls during cognitive reappraisal of unpleasant pictures and passive viewing of unpleasant and neutral pictures. These hypoconnected networks largely overlapped with networks typically activated during the recall of (emotional) autobiographical memories. Functional connectivity strength within these networks significantly increased following treatment and was comparable to controls. Patients showed symptom reduction across various clinical domains and improvement in the use of cognitive reappraisal as emotion regulation strategy. Treatment-related network normalizations were not related to changes in questionnaire data.

Conclusion

Phase-oriented treatment may strengthen connections between regions that are activated during autobiographical recall. These findings encourage further investigation of this circuitry as a therapeutic target in cPTSD and CDD patients.

Clinial trial registration

www.ClinicalTrials.gov, identifier: NCT02459340, https://www.kofam.ch/de/studienportal/suche/149284/studie/26681.

Decoding brain activities of literary metaphor comprehension: An event-related potential and EEG spectral analysis

Novel metaphors in literary texts (hereinafter referred to as literary metaphors) seem to be more creative and open-ended in meaning than metaphors in non-literary texts (non-literary metaphors). However, some disagreement still exists on how literary metaphors differ from non-literary metaphors. Therefore, this study explored the neural mechanisms of literary metaphors extracted from modern Chinese poetry by using the methods of Event-Related Potentials (ERPs) and Event-Related Spectral Perturbations (ERSPs), as compared with non-literary conventional metaphors and literal expressions outside literary texts. Forty-eight subjects were recruited to make the semantic relatedness judgment after reading the prime-target pairs in three linguistic conditions. According to the ERPs results, the earliest differences were presented during the time window of P200 component (170–260 ms) in the frontal and central areas, with the amplitude of P200 for literary metaphors more positive than the other two conditions, reflecting the early allocation of attention and the early conscious experience of the experimental stimuli. Meanwhile, significant differences were presented during the time window of N400 effect (430–530 ms), with the waveform of literary metaphors more negative than others in the frontal and central topography of scalp distributions, suggesting more efforts in retrieving conceptual knowledge for literary metaphors. The ERSPs analysis revealed that the frequency bands of delta and theta were both involved in the cognitive process of literary metaphor comprehension, with delta band distributed in the frontal and central scalp and theta band in parietal and occipital electrodes. Increases in the two power bands during different time windows provided extra evidences that the processing of literary metaphors required more attention and effort than non-literary metaphors and literal expressions in the semantic related tasks, suggesting that the cognitive process of literary metaphors was distinguished by different EEG spectral patterns.

Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning.

Predict Students’ Attention in Online Learning Using EEG Data

In education, it is critical to monitor students’ attention and measure the extents to which students participate and the differences in their levels and abilities. The overall goal of this study was to increase the quality of distance education. In particular, in order to craft an approach that will effectively augment online learning using objective measures of brain activity, we propose a brain–computer interface (BCI) system that aims to use electroencephalography (EEG) signals for the detection of student’s attention during online classes. This system will aid teachers to objectively assess student attention and engagement. To this end, experiments were conducted on a public dataset; we extracted power spectral density (PSD) features using used a fast Fourier transform. Different attention indexes were calculated. Then, we built three different classification algorithms: k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). Our proposed random forest classifier achieved a higher accuracy (96%) than KNN and SVM. Moreover, our results compared to state-of-the-art attention-detection systems with respect to the same dataset. Our findings revealed that the proposed RF approach can be used to effectively distinguish the attention state of a user.

Neural Synchrony and Network Dynamics in Social Interaction: A Hyper-Brain Cell Assembly Hypothesis

Mounting neurophysiological evidence suggests that interpersonal interaction relies on continual communication between cell assemblies within interacting brains and continual adjustments of these neuronal dynamic states between the brains. In this Hypothesis and Theory article, a Hyper-Brain Cell Assembly Hypothesis is suggested on the basis of a conceptual review of neural synchrony and network dynamics and their roles in emerging cell assemblies within the interacting brains. The proposed hypothesis states that such cell assemblies can emerge not only within, but also between the interacting brains. More precisely, the hyper-brain cell assembly encompasses and integrates oscillatory activity within and between brains, and represents a common hyper-brain unit, which has a certain relation to social behavior and interaction. Hyper-brain modules or communities, comprising nodes across two or several brains, are considered as one of the possible representations of the hypothesized hyper-brain cell assemblies, which can also have a multidimensional or multilayer structure. It is concluded that the neuronal dynamics during interpersonal interaction is brain-wide, i.e., it is based on common neuronal activity of several brains or, more generally, of the coupled physiological systems including brains.

Unravelling the Physiological Correlates of Mental Workload Variations in Tracking and Collision Prediction Tasks

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

Cognitive and emotional regulation processes of spontaneous facial self-touch are activated in the first milliseconds of touch: Replication of previous EEG findings and further insights

Spontaneously touching one’s own face (sFST) is an everyday behavior that occurs in people of all ages, worldwide. It is—as opposed to actively touching the own face—performed without directing one’s attention to the action, and it serves neither instrumental (scratching, nose picking) nor communicative purposes. These sFST have been discussed in the context of self-regulation, emotional homeostasis, working memory processes, and attention focus. Even though self-touch research dates back decades, neuroimaging studies of this spontaneous behavior are basically nonexistent. To date, there is only one electroencephalography study that analyzed spectral power changes before and after sFST in 14 participants. The present study replicates the previous study on a larger sample. Sixty participants completed a delayed memory task of complex haptic relief stimuli while distracting sounds were played. During the retention interval 44 of the participants exhibited spontaneous face touch. Spectral power analyses corroborated the results of the replicated study. Decreased power shortly before sFST and increased power right after sFST indicated an involvement of regulation of attentional, emotional, and working memory processes. Additional analyses of spectral power changes during the skin contact phase of sFST revealed that significant neurophysiological changes do not occur while skin contact is in progress but at the beginning of sFST (movement toward face and initial skin contact). The present findings clearly illustrate the complexity of sFST and that the specific trigger mechanisms and functions of this spontaneous behavior need to be further investigated in controlled, experimental studies.

Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG

Wearable EEG has gained popularity in recent years driven by promising uses outside of clinics and research. The ubiquitous application of continuous EEG requires unobtrusive form-factors that are easily acceptable by the end-users. In this progression, wearable EEG systems have been moving from full scalp to forehead and recently to the ear. The aim of this study is to demonstrate that emerging ear-EEG provides similar impedance and signal properties as established forehead EEG. EEG data using eyes-open and closed alpha paradigm were acquired from ten healthy subjects using generic earpieces fitted with three custom-made electrodes and a forehead electrode (at Fpx) after impedance analysis. Inter-subject variability in in-ear electrode impedance ranged from 20 kΩ to 25 kΩ at 10 Hz. Signal quality was comparable with an SNR of 6 for in-ear and 8 for forehead electrodes. Alpha attenuation was significant during the eyes-open condition in all in-ear electrodes, and it followed the structure of power spectral density plots of forehead electrodes, with the Pearson correlation coefficient of 0.92 between in-ear locations ELE (Left Ear Superior) and ERE (Right Ear Superior) and forehead locations, Fp1 and Fp2, respectively. The results indicate that in-ear EEG is an unobtrusive alternative in terms of impedance, signal properties and information content to established forehead EEG.