Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load

Lack of mental integration and emotion dysregulation as a possible long-term effect of dysfunctional parenting: An EEG study of functional connectivity before and after the exposure to attachment-related stimuli

Dysfunctional parenting (DP) is a factor of vulnerability and a predictive risk factor for psychopathology. Although previous research has shown specific functional and structural brain alterations, the neural basis of DP remains understudied. We therefore investigated EEG functional connectivity changes within the Salience Network before and after the exposure to attachment-related stimuli in individuals with high and low perceived DP. Participants (N = 82) were asked to report sociodemographic variables, parenting styles in the first 16 years of life, and individual emotion regulation patterns. A double 5-min EEG recording was conducted with eyes closed, both before and after the Adult Attachment Projective (AAP). Increased connectivity between the anterior cingulate cortex (ACC) and the left supramarginal gyrus (lSMG) in the alpha frequency band was observed exclusively in participants with high perceived DP after the AAP. To understand the functional role of alpha frequency, this band was subdivided into low, medium, and upper alpha. A connectivity analysis was again conducted between the ACC and the lSMG and increased connectivity was observed only in the middle alpha component. A positive correlation was also observed between middle alpha index connectivity and emotional dysregulation exclusively after the activation of the attachment system in individuals with high perceived DP. Our results suggest that individuals with high levels of perceived DP develop specific neurophysiological alterations. These alterations may reflect a lack of mental integration and subsequent emotion dysregulation when exposed to attachment-related, emotionally charged stimuli.

Correlations of pilot trainees' brainwave dynamics with subjective performance evaluations: insights from EEG microstate analysis

Objective

This study aims to investigate the relationship between the subjective performance evaluations on pilot trainees' aircraft control abilities and their brainwave dynamics reflected in the results from EEG microstate analysis. Specifically, we seek to identify correlations between distinct microstate patterns and each dimension included in the subjective flight control evaluations, shedding light on the neurophysiological mechanisms underlying aviation expertise and possible directions for future improvements in pilot training.

Background

Proficiency in aircraft control is crucial for aviation safety and modern aviation where pilots need to maneuver aircraft through an array of situations, ranging from routine takeoffs and landings to complex weather conditions and emergencies. However, the neurophysiological aspects of aviation expertise remain largely unexplored. This research bridges the gap by examining the relationship between pilot trainees' specific brainwave patterns and their subjective evaluations of flight control levels, offering insights into the cognitive underpinnings of pilot skill efficiency and development.

Method

EEG microstate analysis was employed to examine the brainwave dynamics of pilot trainees while they performed aircraft control tasks under a flight simulator-based pilot training process. Trainees' control performance was evaluated by experienced instructors across five dimensions and their EEG data were analyzed to investigate the associations between the parameters of specific microstates with successful aircraft control.

Results

The experimental results revealed significant associations between aircraft control levels and the parameters of distinct EEG microstates. Notably, these associations varied across control dimensions, highlighting the multifaceted nature of control proficiency. Noteworthy correlations included positive correlations between microstate class E and class G with aircraft control, emphasizing the role of attentional processes, perceptual integration, working memory, cognitive flexibility, decision-making, and executive control in aviation expertise. Conversely, negative correlations between microstate class C and class F with aircraft control indicated links between pilot trainees' cognitive control and their control performance on flight tasks.

Conclusion

The findings underscore the multidimensional nature of aircraft control proficiency and emphasize the significance of attentional and cognitive processes in achieving aviation expertise. These neurophysiological markers offer a basis for designing targeted pilot training programs and interventions to enhance trainees' aircraft control skills.

Impact of Experimentally Induced Pain on Logical Reasoning and Underlying Attention-Related Psychophysiological Mechanisms

BACKGROUND

Pain is known to negatively impact attention, but its influence on more complex cognitive abilities, such as logical reasoning, remains inconsistent. This may be due to compensatory mechanisms (e.g., investing additional resources), which might not be detectable at the behavioral level but can be observed through psychophysiological measures. In this study, we investigated whether experimentally induced pain affects logical reasoning and underlying attentional mechanisms, using both behavioral and electroencephalographic (EEG) measures.

METHODS

A total of 98 female participants were divided into a pain-free control group (N = 47) and a pain group (N = 51). Both groups completed the Advanced Progressive Matrices (APM) task, with EEG recordings capturing task-related power (TRP) changes in the upper alpha frequency band (10-12 Hz). We used a mixed design where all participants completed half of the APM task in a pain-free state (control condition); the second half was completed under pain induction by the pain group but not the pain-free group (experimental condition).

RESULTS

Logical reasoning performance, as measured by APM scores and response times, declined during the experimental condition, compared to the control condition for both groups, indicating that the second part of the APM was more difficult than the first part. However, no significant differences were found between the pain and pain-free groups, suggesting that pain did not impair cognitive performance at the behavioral level. In contrast, EEG measures revealed significant differences in upper alpha band power, particularly at fronto-central sites. In the pain group, the decrease in TRP during the experimental condition was significantly smaller compared to both the control condition and the pain-free group.

CONCLUSIONS

Pain did not impair task performance at the behavioral level but reduced attentional resources, as reflected by changes in upper alpha band activity. This underscores the importance of incorporating more sensitive psychophysiological measures alongside behavioral measures to better understand the impact of pain on cognitive processes.

Monitoring pilot trainees' cognitive control under a simulator-based training process with EEG microstate analysis

The objective of pilot training is to equip trainees with the knowledge, judgment, and skills to maintain control of an aircraft and respond to critical flight tasks. The present research aims to investigate changes in trainees' cognitive control levels during a pilot training process while they underwent basic flight maneuvers. EEG microstate analysis was applied together with spectral power features to quantitatively monitor trainees' cognitive control under varied flight tasks during different training sessions on a flight simulator. Not only could EEG data provide an objective measure of cognitive control to complement the current subjective assessments, but the application of EEG microstate analysis is particularly well-suited for capturing rapid dynamic changes in cognitive states that may happen under complex human activities in conducting flight maneuvers. Comparisons were conducted between two types of tasks and across different training stages to monitor how pilot trainees' cognitive control responds to varied flight task types and training stages. The present research provides insights into the changes in trainees' cognitive control during a pilot training process and highlights the potential of EEG microstate analysis for monitoring cognitive control.

Enhancing learning experiences: EEG-based passive BCI system adapts learning speed to cognitive load in real-time, with motivation as catalyst

Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner's mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner's cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann-Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI's potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.

Working memory processes and intrinsic motivation: An EEG study

Processes typically encompassed by working memory (WM) include encoding, retention, and retrieval of information. Previous research has demonstrated that motivation can influence WM performance, although the specific WM processes affected by motivation are not yet fully understood. In this study, we investigated the effects of motivation on different WM processes, examining how task difficulty modulates these effects. We hypothesized that motivation level and personality traits of the participants (N = 48, 32 females; mean age = 21) would modulate the parietal alpha and frontal theta electroencephalography (EEG) correlates of WM encoding, retention, and retrieval phases of the Sternberg task. This effect was expected to be more pronounced under conditions of very high task difficulty. We found that increasing difficulty led to reduced accuracy and increased response time, but no significant relationship was found between motivation and accuracy. However, EEG data revealed that motivation influenced WM processes, as indicated by changes in alpha and theta oscillations. Specifically, higher levels of the Resilience trait-associated with mental toughness, hardiness, self-efficacy, achievement motivation, and low anxiety-were related to increased alpha desynchronization during encoding and retrieval. Increased scores of Subjective Motivation to perform well in the task were related to enhanced frontal midline theta during retention. Additionally, these effects were significantly stronger under conditions of high difficulty. These findings provide insights into the specific WM processes that are influenced by motivation, and underscore the importance of considering both task difficulty and intrinsic motivation in WM research.

The influence of adverse childhood experiences and depression on addiction severity among methamphetamine users: exploring the role of perseveration

Aims

This investigation aimed to clarify the intricate relationship among depression, cognitive function, adverse childhood experiences (ACEs), and their combined influence on methamphetamine use disorder (MUD).

Methods

Utilizing a battery of psychological tests, this study ascertained the impact of ACEs on the condition of 76 people with MUD who meet the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria, aged 42.17 on average. The Iowa Gambling Task (IGT), Conners' Continuous Performance-II (CPT-II), the self-report Severity of Dependence Scale (SDS), and the Beck Depression Inventory-II (BDI-II) were used for these evaluations. Individuals involved in the study were categorized into two discrete cohorts, mild (ME) and severe (SE), based on the extent of their ACEs exposure. This study employed the PROCESS regression, the independent t-test andχ2 tests for the analysis.

Results

The findings revealed notable discrepancies in the psychological consequences between the two groups with different degrees of ACEs; however, no substantial differences were observed in the demographic parameters. The SE group exhibited elevated BDI-II scores, more evident indications of MUD, and a higher degree of CPT-II cognitive perseveration. The PROCESS model revealed that cognitive perseveration moderated the impact of depression on ACEs and subjective MUD severity, explaining 20.2% of the variance. The ACEs and depression predicted 28.6% of the variance in MUD symptoms. However, no statistically significant differences were detected between the two groups regarding the parameters in the IGT-2 assessment.

Conclusions

These results indicate that the interaction between cognitive and depressive factors mediates the effect of ACEs on subjective MUD severity but not on MUD symptoms. The ACEs significant impact on mental health severity perception is explained by cognitive and depressive factors. This implies that MUD treatment and rehabilitation should address cognitive dysfunction and developmental trauma.

The Impact of Various Cockpit Display Interfaces on Novice Pilots' Mental Workload and Situational Awareness: A Comparative Study

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots.

Limited Consistency and Strength of Neural Oscillations During Sustained Visual Attention in Schizophrenia

BACKGROUND

Neural oscillations support perception, attention, and higher-order decision making. Aberrations in the strength or consistency of these oscillations in response to stimuli may underlie impaired visual perception and attention in schizophrenia. Here, we examined the phase and power of alpha oscillations (8-12 Hz) as well as aspects of beta and theta frequency oscillations during a demanding visual sustained attention task.

METHODS

Patients with schizophrenia (n = 74) and healthy control participants (n = 68) completed the degraded stimulus continuous performance task during electroencephalography. We used time-frequency analysis to evaluate the consistency (intertrial phase coherence) of the alpha cycle shortly after stimulus presentation (50-250 ms). For oscillation strength, we examined event-related desynchronization in a later window associated with decision making (360-700 ms).

RESULTS

Alpha intertrial phase coherence was reduced in schizophrenia, and similar reductions were observed in theta (4-7 Hz) and beta (13-20 Hz), suggesting a lack of responsiveness in slower oscillations to visual stimuli. Alpha and beta event-related desynchronization were also reduced in schizophrenia and associated with worse task performance, increased symptoms, and poorer cognition, suggesting that limited responsiveness of oscillations is related to impairments in the disorder. Individuals with lower intertrial phase coherence had slower resting-state alpha rhythms consistent with dysfunctional oscillations persisting across default and task-related brain states.

CONCLUSIONS

In schizophrenia, abnormalities in the phase consistency and strength of slower oscillations during visual perception are related to symptoms and cognitive functioning. Altered visual perception and impaired attention in the disorder may be the consequence of aberrant slower oscillations that fail to dynamically reset and modulate in response to stimuli.

EEG-based neurophysiological indices for expert psychomotor performance - a review

A primary objective of current human neuropsychological performance research is to define the physiological correlates of adaptive knowledge utilization, in order to support the enhanced execution of both simple and complex tasks. Within the present article, electroencephalography-based neurophysiological indices characterizing expert psychomotor performance, will be explored. As a means of characterizing fundamental processes underlying efficient psychometric performance, the neural efficiency model will be evaluated in terms of alpha-wave-based selective cortical processes. Cognitive and motor domains will initially be explored independently, which will act to encapsulate the task-related neuronal adaptive requirements for enhanced psychomotor performance associating with the neural efficiency model. Moderating variables impacting the practical application of such neuropsychological model, will also be investigated. As a result, the aim of this review is to provide insight into detectable task-related modulation involved in developed neurocognitive strategies which support heightened psychomotor performance, for the implementation within practical settings requiring a high degree of expert performance (such as sports or military operational settings).

Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes

Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.

Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load

Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load - a common cognitive manipulation in psychology - as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.

Evidence of Chaos in Electroencephalogram Signatures of Human Performance: A Systematic Review

(1) Background: Chaos, a feature of nonlinear dynamical systems, is well suited for exploring biological time series, such as heart rates, respiratory records, and particularly electroencephalograms. The primary purpose of this article is to review recent studies using chaos theory and nonlinear dynamical methods to analyze human performance in different brain processes. (2) Methods: Several studies have examined chaos theory and related analytical tools for describing brain dynamics. The present study provides an in-depth analysis of the computational methods that have been proposed to uncover brain dynamics. (3) Results: The evidence from 55 articles suggests that cognitive function is more frequently assessed than other brain functions in studies using chaos theory. The most frequently used techniques for analyzing chaos include the correlation dimension and fractal analysis. Approximate, Kolmogorov and sample entropy account for the largest proportion of entropy algorithms in the reviewed studies. (4) Conclusions: This review provides insights into the notion of the brain as a chaotic system and the successful use of nonlinear methods in neuroscience studies. Additional studies of brain dynamics would aid in improving our understanding of human cognitive performance.

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG

Objective.Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during reasoning learning using EEG, and aims to fill this gap with a multidisciplinary approach combining educational psychology, neuroscience and computer science.Approach.First, we design an experiment to actively and accurately induce confusion in reasoning. Second, we propose a subjective and objective joint labeling technique to address the label noise issue. Third, to confirm that the confused state can be distinguished from the non-confused state, we compare and analyze the mean band power of confused and unconfused states across five typical bands. Finally, we present an EEG database for confusion analysis, together with benchmark results from conventional (Naive Bayes, Support Vector Machine, Random Forest, and Artificial Neural Network) and end-to-end (Long Short Term Memory, Residual Network, and EEGNet) machine learning methods.Main results.Findings revealed: 1. Significant differences in the power of delta, theta, alpha, beta and lower gamma between confused and non-confused conditions; 2. A higher attentional and cognitive load when participants were confused; and 3. The Random Forest algorithm with time-domain features achieved a high accuracy/F1 score (88.06%/0.88 for the subject-dependent approach and 84.43%/0.84 for the subject-independent approach) in the binary classification of the confused and non-confused states.Significance.The study advances our understanding of confusion and provides practical insights for recognizing and analyzing it in the learning process. It extends existing theories on the differences between confused and non-confused states during learning and contributes to the cognitive-affective model. The research enables researchers, educators, and practitioners to monitor confusion, develop adaptive systems, and test recognition approaches.

The spectral profile of cortical activation during a visuospatial mental rotation task and its correlation with working memory

Introduction

The search for a cortical signature of intelligent behavior has been a longtime motivation in Neuroscience. One noticeable characteristic of intelligence is its association with visuospatial skills. This has led to a steady focus on the functional and structural characteristics of the frontoparietal network (FPN) of areas involved with higher cognition and spatial behavior in humans, including the question of whether intelligence is correlated with larger or smaller activity in this important cortical circuit. This question has broad significance, including speculations about the evolution of human cognition. One way to indirectly measure cortical activity with millisecond precision is to evaluate the event-related spectral perturbation (ERSP) of alpha power (alpha ERSP) during cognitive tasks. Mental rotation, or the ability to transform a mental representation of an object to accurately predict how the object would look from a different angle, is an important feature of everyday activities and has been shown in previous work by our group to be positively correlated with intelligence. In the present work, we evaluate whether alpha ERSP recorded over the parietal, frontal, temporal, and occipital regions of adolescents performing easy and difficult trials of the Shepard–Metzler’s mental rotation task, correlates or are predicted by intelligence measures of the Weschler’s intelligence scale.

Methods

We used a database obtained from a previous study of intellectually gifted (N = 15) and average intelligence (N = 15) adolescents.

Results

Our findings suggest that in challenging task conditions, there is a notable difference in the prominence of alpha event-related spectral perturbation (ERSP) activity between various cortical regions. Specifically, we found that alpha ERSP in the parietal region was less prominent relative to those in the frontal, temporal and occipital regions. Working memory scores predict alpha ERSP values in the frontal and parietal regions. In the frontal cortex, alpha ERSP of difficult trials was negatively correlated with working memory scores.

Discussion

Thus, our results suggest that even though the FPN is task-relevant during mental rotation tasks, only the frontal alpha ERSP is correlated with working memory score in mental rotation tasks.

Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

BACKGROUND AND OBJECTIVE

Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs).

METHODS

Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis.

RESULTS

MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively).

CONCLUSIONS

The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.

Reliability of MUSE 2 and Tobii Pro Nano at capturing mobile application users' real-time cognitive workload changes

Introduction

Despite the importance of cognitive workload in examining the usability of smartphone applications and the popularity of smartphone usage globally, cognitive workload as one attribute of usability tends to be overlooked in Human-Computer Interaction (HCI) studies. Moreover, limited studies that have examined the cognitive workload aspect often measured some summative workloads using subjective measures (e.g., questionnaires). A significant limitation of subjective measures is that they can only assess the overall, subject-perceived cognitive workload after the procedures/tasks have been completed. Such measurements do not reflect the real-time workload fluctuation during the procedures. The reliability of some devices on a smartphone setting has not been thoroughly evaluated.

Methods

This study used mixed methods to empirically study the reliability of an eye-tracking device (i.e., Tobii Pro Nano) and a low-cost electroencephalogram (EEG) device (i.e., MUSE 2) for detecting real-time cognitive workload changes during N-back tasks.

Results

Results suggest that the EEG measurements collected by MUSE 2 are not very useful as indicators of cognitive workload changes in our setting, eye movement measurements collected by Tobii Pro Nano with mobile testing accessory are useful for monitoring cognitive workload fluctuations and tracking down interface design issues in a smartphone setting, and more specifically, the maximum pupil diameter is the preeminent indicator of cognitive workload surges.

Discussion

In conclusion, the pupil diameter measure combined with other subjective ratings would provide a comprehensive user experience assessment of mobile applications. They can also be used to verify the successfulness of a user interface design solution in improving user experience.

Intelligence and Visual Mismatch Negativity: Is Pre-Attentive Visual Discrimination Related to General Cognitive Ability?

EEG has been used for decades to identify neurocognitive processes related to intelligence. Evidence is accumulating for associations with neural markers of higher-order cognitive processes (e.g., working memory); however, whether associations are specific to complex processes or also relate to earlier processing stages remains unclear. Addressing these issues has implications for improving our understanding of intelligence and its neural correlates. The MMN is an ERP that is elicited when, within a series of frequent standard stimuli, rare deviant stimuli are presented. As stimuli are typically presented outside the focus of attention, the MMN is suggested to capture automatic pre-attentive discrimination processes. However, the MMN and its relation to intelligence has largely only been studied in the auditory domain, thus preventing conclusions about the involvement of automatic discrimination processes in humans' dominant sensory modality-vision. EEG was recorded from 50 healthy participants during a passive visual oddball task that presented simple sequence violations and deviations within a more complex hidden pattern. Signed area amplitudes and fractional area latencies of the visual MMN were calculated with and without Laplacian transformation. Correlations between visual MMN and intelligence (Raven's Advanced Progressive Matrices) were of negligible to small effect sizes, differed critically between measurement approaches, and Bayes Factors provided anecdotal to substantial evidence for the absence of an association. We discuss differences between the auditory and visual MMN, the implications of different measurement approaches, and offer recommendations for further research in this evolving field.

The influence of experience on cognitive load during simultaneous interpretation

Simultaneous interpretation is a complex task that is assumed to be associated with a high workload. To corroborate this association, we measured workload during three tasks of increasing complexity: listening, shadowing, and interpreting, using electroencephalography and self-assessments in four groups of participants with varying experience in simultaneous interpretation. The self-assessment data showed that professional interpreters perceived the most workload-inducing condition, namely the interpreting task, as less demanding compared to the less experienced participants. This higher subjectively perceived workload in non-interpreters was paralleled by increasing frontal theta power values from listening to interpreting, whereas such a modulation was less pronounced in professional interpreters. Furthermore, regarding both workload measures, trainee interpreters were situated between professional interpreters and non-interpreters. Since the non-interpreters demonstrated high proficiencies and exposure in their second language, too, our findings provide evidence for an influence of interpretation training on experienced workload during simultaneous interpretation.

Modeling Cognitive Load as a Self-Supervised Brain Rate with Electroencephalography and Deep Learning

The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing a continuous brain rate, an index of cognitive activation, and does not require human declarative knowledge. The aim is to induce models automatically from data, supporting replicability, generalisability and applicability across fields and contexts. This specific method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from EEG data, aimed at fitting a novel brain rate variable. Findings demonstrate the capacity of the convolutional layers to learn meaningful high-level representations from EEG data since within-subject models had, on average, a test Mean Absolute Percentage Error of around 11%. The addition of a Long-Short Term Memory layer for handling sequences of high-level representations was not significant, although it did improve their accuracy. These findings point to the existence of quasi-stable blocks of automatically learnt high-level representations of cognitive activation because they can be induced through convolution and seem not to be dependent on each other over time, intuitively matching the non-stationary nature of brain responses. Additionally, across-subject models, induced with data from an increasing number of participants, thus trained with data containing more variability, obtained a similar accuracy to the within-subject models. This highlights the potential generalisability of the induced high-level representations across people, suggesting the existence of subject-independent cognitive activation patterns. This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable and does not rely on ad hoc human crafted models.

EEG based functional brain networks analysis in dyslexic children during arithmetic task

Developmental Dyslexia is a neuro-developmental disorder that often refers to a phonological processing deficit regardless of average IQ. The present study investigated the distinct functional changes in brain networks of dyslexic children during arithmetic task performance using an electroencephalogram. Fifteen dyslexic children and fifteen normally developing children (NDC) were recruited and performed an arithmetic task. Brain functional network measures such as node strength, clustering coefficient, characteristic pathlength and small-world were calculated using graph theory methods for both groups. Task performance showed significantly less performance accuracy in dyslexics against NDC. The neural findings showed increased connectivity in the delta band and reduced connectivity in theta, alpha, and beta band at temporoparietal, and prefrontal regions in dyslexic group while performing the task. The node strengths were found to be significantly high in delta band (T3, O1, F8 regions) and low in theta (T5, P3, Pz regions), beta (Pz) and gamma band (T4 and prefrontal regions) during the task in dyslexics compared to the NDC. The clustering coefficient was found to be significantly low in the dyslexic group (theta and alpha band) and characteristic pathlength was found to be significantly high in the dyslexic group (theta and alpha band) compared to the NDC group while performing task. In conclusion, the present study shows evidence for poor fact-retrieval mechanism and altered network topology in dyslexic brain networks during arithmetic task performance.

Parietal Alpha Oscillations: Cognitive Load and Mental Toughness

Cognitive effort is intrinsically linked to task difficulty, intelligence, and mental toughness. Intelligence reflects an individual’s cognitive aptitude, whereas mental toughness (MT) reflects an individual’s resilience in pursuing success. Research shows that parietal alpha oscillations are associated with changes in task difficulty. Critically, it remains unclear whether parietal alpha oscillations are modulated by intelligence and MT as a personality trait. We examined event-related (de)synchronization (ERD/ERS) of alpha oscillations associated with encoding, retention, and recognition in the Sternberg task in relation to intelligence and mental toughness. Eighty participants completed the Sternberg task with 3, 4, 5 and 6 digits, Raven Standard Progressive Matrices test and an MT questionnaire. A positive dependence on difficulty was observed for all studied oscillatory effects (t = −8.497, p < 0.001; t = 2.806, p < 0.005; t = −2.103, p < 0.05). The influence of Raven intelligence was observed for encoding-related alpha ERD (t = −2.02, p = 0.049). The influence of MT was observed only for difficult conditions in recognition-related alpha ERD (t = −3.282, p < 0.005). Findings indicate that the modulation of alpha rhythm related to encoding, retention and recognition may be interpreted as correlates of cognitive effort modulation. Specifically, results suggest that effort related to encoding depends on intelligence, whereas recognition-related effort level depends on mental toughness.

Effects of spatialized water-sound sequences for traffic noise masking on brain activities

Informational masking of water sounds has been proven effective in mitigating traffic noise perception with different sound levels and signal-to-noise ratios, but less is known about the effects of the spatial distribution of water sounds on the perception of the surrounding environment and corresponding psychophysical responses. Three different spatial settings of water-sound sequences with a traffic noise condition were used to investigate the role of spatialization of water-sound sequences on traffic noise perception. The neural responses of 20 participants were recorded by a portable electroencephalogram (EEG) device during the spatial sound playback time. The mental effects and attention process related to informational masking were assessed by the analysis of the EEG spectral power distribution and sensor-level functional connectivity along with subjective assessments. The results showed higher relative power of the alpha band and greater alpha-beta ratio among water-sound sequence conditions compared to traffic noise conditions, which confirmed the increased relaxation on the mental state induced by the introduction of water sounds. Moreover, different spatial settings of water-sound sequences evoked different cognitive network responses. The setting of two-position switching water brought more attentional network activations than other water sequences related to the information masking process along with more positive subjective feelings.

Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Industrial settings will be characterized by far-reaching production automation brought about by advancements in robotics and artificial intelligence. As a consequence, human assembly workers will need to adapt quickly to new and more complex assembly procedures, which are most likely to increase cognitive workload, or potentially induce overload. Measurement and optimization protocols need to be developed in order to be able to monitor workers' cognitive load. Previous studies have used electroencephalographic (EEG, measuring brain activity) and electrooculographic (EOG, measuring eye movements) signals, using basic computer-based static tasks and without creating an experience of overload. In this study, EEG and EOG data was collected of 46 participants performing an ecologically valid assembly task while inducing three levels of cognitive load (low, high and overload). The lower individual alpha frequency (IAF) was identified as a promising marker for discriminating between different levels of cognitive load and overload.

Language as the Working Model of Human Mind

The Human Mind, functional aspect of Human Brain, has been envisaged to be working on the tenets of Chaos, a seeming order within a disorder, the premise of Universe. The armamentarium of Human Mind makes use of distributed neuronal networks sub-serving Sensorial Mechanisms, Mirror Neurone System (MNS) and Motor Mechanisms etching a stochastic trajectory on the virtual phase-space of Human Mind, obeying the ethos of Chaos. The informational sensorial mechanisms recruit attentional mechanisms channelising through the window of chaotic neural dynamics onto MNS that providing algorithmic image information flow along virtual phase- space coordinates concluding onto motor mechanisms that generates and mirrors a stimulus- specific and stimulus-adequate response. The singularity of self-iterating fractal architectonics of Event-Related Synchrony (ERS), a Power Spectral Density (PSD) precept of electroencephalographic (EEG) time-series denotes preferential and categorical inhibition gateway and an Event-Related Desynchrony (ERD) represents event related and locked gateway to stimulatory/excitatory neuronal architectonics leading to stimulus-locked and adequate neural response. The contextual inference in relation to stochastic phase-space trajectory of self- iterating fractal of Off-Center α ERS (Central)-On-Surround α ERD-On Surround θ ERS document efficient neural dynamics of working memory., across patterned modulation and flow of the neurally coded information.

Development of Modular and Adaptive Laboratory Set-Up for Neuroergonomic and Human-Robot Interaction Research

The industry increasingly insists on academic cooperation to solve the identified problems such as workers' performance, wellbeing, job satisfaction, and injuries. It causes an unsafe and unpleasant working environment that directly impacts the quality of the product, workers' productivity, and effectiveness. This study aimed to give a specialized solution for tests and explore possible solutions to the given problem in neuroergonomics and human–robot interaction. The designed modular and adaptive laboratory model of the industrial assembly workstation represents the laboratory infrastructure for conducting advanced research in the field of ergonomics, neuroergonomics, and human–robot interaction. It meets the operator's anatomical, anthropometric, physiological, and biomechanical characteristics. Comparing standard, ergonomic, guided, and collaborative work will be possible based on workstation construction and integrated elements. These possibilities allow the industry to try, analyze, and get answers for an identified problem, the condition, habits, and behavior of operators in the workplace. The set-up includes a workstation with an industry work chair, a Poka–Yoke system, adequate lighting, an audio 5.0 system, containers with parts and tools, EEG devices (a cap and smartfones), an EMG device, touchscreen PC screen, and collaborative robot. The first phase of the neuroergonomic study was performed according to the most common industry tasks defined as manual, monotonous, and repetitive activities. Participants have a task to assemble the developed prototype model of an industrial product using prepared parts and elements, and instructed by the installed touchscreen PC. In the beginning, the participant gets all the necessary information about the experiment and gets 15 min of practice. After the introductory part, the EEG device is mounted and prepared for recording. The experiment starts with relaxing music for 5 min. The whole experiment lasts two sessions per 60 min each, with a 15 min break between the sessions. Based on the first experiments, it is possible to develop, construct, and conduct complex experiments for industrial purposes to improve the physical, cognitive, and organizational aspects and increase workers' productivity, efficiency, and effectiveness. It has highlighted the possibility of applying modular and adaptive ergonomic research laboratory experimental set-up to transform standard workplaces into the workplaces of the future.

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.

“Sound” Decisions: The Combined Role of Ambient Noise and Cognitive Regulation on the Neurophysiology of Food Cravings

Our ability to evaluate long-term goals over immediate rewards is manifested in the brain’s decision circuit. Simplistically, it can be divided into a fast, impulsive, reward “system 1” and a slow, deliberate, control “system 2.” In a noisy eating environment, our cognitive resources may get depleted, potentially leading to cognitive overload, emotional arousal, and consequently more rash decisions, such as unhealthy food choices. Here, we investigated the combined impact of cognitive regulation and ambient noise on food cravings through neurophysiological activity. Thirty-seven participants were recruited for an adapted version of the Regulation of Craving (ROC) task. All participants underwent two sessions of the ROC task; once with soft ambient restaurant noise (∼50 dB) and once with loud ambient restaurant noise (∼70 dB), while data from electroencephalography (EEG), electrodermal activity (EDA), and self-reported craving were collected for all palatable food images presented in the task. The results indicated that thinking about future (“later”) consequences vs. immediate (“now”) sensations associated with the food decreased cravings, which were mediated by frontal EEG alpha power. Likewise, “later” trials also increased frontal alpha asymmetry (FAA) —an index for emotional motivation. Furthermore, loud (vs. soft) noise increased alpha, beta, and theta activity, but for theta activity, this was solely occurring during “later” trials. Similarly, EDA signal peak probability was also higher during loud noise. Collectively, our findings suggest that the presence of loud ambient noise in conjunction with prospective thinking can lead to the highest emotional arousal and cognitive load as measured by EDA and EEG, respectively, both of which are important in regulating cravings and decisions. Thus, exploring the combined effects of interoceptive regulation and exteroceptive cues on food-related decision-making could be methodologically advantageous in consumer neuroscience and entail theoretical, commercial, and managerial implications.

High fluid intelligence is characterized by flexible allocation of attentional resources: Evidence from EEG

Recently, the integrated control hypothesis (Lu et al., 2020) was proposed to explain the relationship between fluid intelligence (Gf) and attentional resource allocation. This hypothesis suggested that individuals with higher Gf tend to flexibly and adaptively allocate their limited resources according to the task type and task difficulty rather than simply exert more or fewer resources in any condition. To examine this hypothesis, the present study used electroencephalogram (EEG) indicators (i.e., frontal theta-ERS and parietal-occipital alpha-ERD) as the measurements of participants' resource allocation during the exploration task and exploitation task with different difficulties. The results found that higher Gf individuals tend to allocate fewer resources in all difficulty levels in the exploitation task compared to average Gf participants. In contrast, in the exploration task, higher Gf participants would allocate more resources in the medium- and high-difficulty levels than average Gf participants, but this phenomenon was only found in males. These findings provided supportive evidence for the integrated control hypothesis that flexible and adaptive attentional control ability are important characteristics of human intelligence.

Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks

Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.

Mental State Detection Using Riemannian Geometry on Electroencephalogram Brain Signals

The goal of this study was to implement a Riemannian geometry (RG)-based algorithm to detect high mental workload (MWL) and mental fatigue (MF) using task-induced electroencephalogram (EEG) signals. In order to elicit high MWL and MF, the participants performed a cognitively demanding task in the form of the letter n-back task. We analyzed the time-varying characteristics of the EEG band power (BP) features in the theta and alpha frequency band at different task conditions and cortical areas by employing a RG-based framework. MWL and MF were considered as too high, when the Riemannian distances of the task-run EEG reached or surpassed the threshold of the baseline EEG. The results of this study showed a BP increase in the theta and alpha frequency bands with increasing experiment duration, indicating elevated MWL and MF that impedes/hinders the task performance of the participants. High MWL and MF was detected in 8 out of 20 participants. The Riemannian distances also showed a steady increase toward the threshold with increasing experiment duration, with the most detections occurring toward the end of the experiment. To support our findings, subjective ratings (questionnaires concerning fatigue and workload levels) and behavioral measures (performance accuracies and response times) were also considered.

Study of EEG characteristics while solving scientific problems with different mental effort

Studying the mental effort in problem-solving is important to the understanding of how the brain allocates cognitive resources to process information. The electroencephalogram is a promising physiological approach to assessing the online mental effort. In this study, we investigate the EEG indicators of mental effort while solving scientific problems. By manipulating the complexity of the scientific problem, the level of mental effort also changes. With the increase of mental effort, theta synchronization in the frontal region and lower alpha desynchronization in the parietal and occipital regions significantly increase. Also, upper alpha desynchronization demonstrates a widespread enhancement across the whole brain. According to the functional topography of brain activity in the theta and alpha frequency, our results suggest that the mental effort while solving scientific problems is related to working memory, visuospatial processing, semantic processing and magnitude manipulation. This study suggests the reliability of EEG to evaluate the mental effort in an educational context and provides valuable insights into improving the problem-solving abilities of students in educational practice.

Model-Based Planning Deficits in Compulsivity Are Linked to Faulty Neural Representations of Task Structure

Compulsive individuals have deficits in model-based planning, but the mechanisms that drive this have not been established. We examined two candidates-that compulsivity is linked to (1) an impaired model of the task environment and/or (2) an inability to engage cognitive control when making choices. To test this, 192 participants performed a two-step reinforcement learning task with concurrent EEG recordings, and we related the neural and behavioral data to their scores on a self-reported transdiagnostic dimension of compulsivity. To examine subjects' internal model of the task, we used established behavioral and neural responses to unexpected events [reaction time (RT) slowing, P300 wave, and parietal-occipital alpha band power] measured when an unexpected transition occurred. To assess cognitive control, we probed theta power at the time of initial choice. As expected, model-based planning was linked to greater behavioral (RT) and neural (alpha power, but not P300) sensitivity to rare transitions. Critically, the sensitivities of both RT and alpha to task structure were weaker in those high in compulsivity. This RT-compulsivity effect was tested and replicated in an independent pre-existing dataset (N = 1413). We also found that mid-frontal theta power at the time of choice was reduced in highly compulsive individuals though its relation to model-based planning was less pronounced. These data suggest that model-based planning deficits in compulsive individuals may arise, at least in part, from having an impaired representation of the environment, specifically how actions lead to future states.SIGNIFICANCE STATEMENT Compulsivity is linked to poorer performance on tasks that require model-based planning, but it is unclear what precise mechanisms underlie this deficit. Do compulsive individuals fail to engage cognitive control at the time of choice? Or do they have difficulty in building and maintaining an accurate representation of their environment, the foundation needed to behave in a goal-directed manner? With reaction time and EEG measures in 192 individuals who performed a two-step decision-making task, we found that compulsive individuals are less sensitive to surprising action-state transitions, where they slow down less and show less alpha band suppression following a rare transition. These findings implicate failures in maintaining an accurate model of the world in model-based planning deficits in compulsivity.

The Role of Alpha Power in the Suppression of Anticipated Distractors During Verbal Working Memory

As working memory (WM) is limited in capacity, it is important to direct neural resources towards processing task-relevant information while ignoring distractors. Neural oscillations in the alpha frequency band (8-12 Hz) have been suggested to play a role in the inhibition of task-irrelevant information during WM, although results are mixed, possibly due to differences in the type of WM task employed. Here, we examined the role of alpha power in suppression of anticipated distractors of varying strength using a modified Sternberg task where the encoding and retention periods were temporally separated. We recorded EEG while 20 young adults completed the task and found: (1) slower reaction times in strong distractor trials compared to weak distractor trials; (2) increased alpha power in posterior regions from baseline prior to presentation of a distractor regardless of condition; and (3) no differences in alpha power between strong and weak distractor conditions. Our results suggest that parieto-occipital alpha power is increased prior to a distractor. However, we could not find evidence that alpha power is further modulated by distractor strength.

Working Memory in Children with Learning Disorders: An EEG Power Spectrum Analysis

Learning disorders (LDs) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagging according to their age, schooling and intelligence. Children with LDs experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related electroencephalogram (EEG) power spectral density of children with LDs (n = 23) with a control group of children with good academic achievement (n = 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interest (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessened some drawbacks of the traditional EEG at the sensor space by an analysis at the brain-sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses in the WM task, an overall slower EEG with more delta and theta activity, and less high-frequency gamma activity in posterior areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in neural maturation.

Load-dependent modulation of alpha oscillations during working memory encoding and retention in young and older adults

Working memory (WM) is vulnerable to age-related decline, particularly under high loads. Visual alpha oscillations contribute to WM performance in younger adults, and although alpha decreases in power and frequency with age, it is unclear if alpha activity supports WM in older adults. We recorded electroencephalography (EEG) while 24 younger (aged 18-35 years) and 30 older (aged 50-86) adults performed a modified Sternberg task with varying load conditions. Older adults demonstrated slower reaction times at all loads, but there were no significant age differences in WM capacity. Regardless of age, alpha power decreased and alpha frequency increased with load during encoding, and the magnitude of alpha suppression during retention was larger at higher loads. While alpha power during retention was lower than fixation in older, but not younger adults, the relative change from fixation was not significantly different between age groups. Individual differences in alpha power did not predict performance for either age groups or at any WM loads. We demonstrate that alpha power and frequency are modulated in a similar task- and load-dependent manner during WM in both older and younger adults when WM performance is comparable across age groups. IMPACT STATEMENT: Aging is associated with a marked decrease in the power and frequency of alpha oscillations. Here, we demonstrate that when verbal working memory performance is matched across age groups, alpha power and frequency are modulated in a similar task- and load-dependent manner in both young and older adults.

Brain activity during a working memory task in different postures: an EEG study

While working is more comfortable in a supine position and healthier in a standing, most people work in a sitting. However, it is unclear whether there are differences in brain activity efficiency in different postures. Here, we, therefore, compared changes in brain activity across three different postures to determine the optimal posture for performing working memory tasks. Their effect on brain activity was examined using EEG signals together with the information of accuracy and reaction times during 2-back task in 24 subjects. Substantial differences in brain waves were observed at sitting and standing positions compared to the supine, especially in delta waves and frontal lobe, where is known to improve the modulation of brain activity efficiently. Brain efficiency was higher during standing and sitting than in a supine. These findings show that postural changes may affect the efficiency of brain activity during working memory tasks. Practitioner summary: Differences in brain efficiency between different postures during working memory tasks have not been explored. This study suggests that efficiency in several brain areas is higher during sitting and standing than in a supine position. This finding has important implications regarding workplace environments. Furthermore, this result would be useful to improve accomplishment and reduce negative effects of work posture. Abbreviations: EEG: electroencephalogram; PSQI: Pittsburgh sleep quality index; KSS: Karolinska sleepiness scale; FFT: fast fourier transform; ROI: region of interest; ANS: autonomic nervous system; Fp: prefrontal; AF: anterior frontal; frontal; Fz: midline frontal; temporal; central; Cz: midline central; P: parietal; Pz: midline parietal; O: occipital; Oz: midline occipital.

Prediction of Antidepressant Treatment Outcome Using Event-Related Potential in Patients with Major Depressive Disorder

(1) Background: Prediction of treatment outcome has been one of the core objectives in clinical research of patients with major depressive disorder (MDD). This study explored the possibility of event-related potential (ERP) markers to predict antidepressant treatment outcomes among MDD patients; (2) Methods: Fifty-two patients with MDD were recruited and evaluated through Hamilton depression (HAM-D), Hamilton anxiety rating scale (HAM-A), and CORE. Patients underwent a battery of ERP measures including frontal alpha symmetry (FAA) in the low alpha band (8–10 Hz), mismatch negativity (MMN), and loudness-dependent auditory evoked potentials (LDAEP); (3) Results: During the eight weeks of study, 61% of patients achieved remission, and 77% showed successful treatment responsiveness. Patients with low FAA in F5/F6 demonstrated a significantly higher remission/response ratio and better treatment responsiveness (F (2.560, 117.755) = 3.84, p = 0.016) compared to patients with high FAA. In addition, greater FAA in F7/F8 EEG channels was significantly associated with greater melancholia scores (r = 0.34, p = 0.018). Other ERP markers lacked any significant effect; (4) Conclusions: Our results suggested low FAA (i.e., greater left frontal activity) could reflect a good treatment response in MDD patients. These findings support that FAA could be a promising index in understanding both MDD and melancholic subtype.

EEG alpha reactivity and cholinergic system integrity in Lewy body dementia and Alzheimer's disease

BACKGROUND

Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), is characterised by marked deficits within the cholinergic system which are more severe than in Alzheimer's disease (AD) and are mainly caused by degeneration of the nucleus basalis of Meynert (NBM) whose widespread cholinergic projections provide the main source of cortical cholinergic innervation. EEG alpha reactivity, which refers to the reduction in alpha power over occipital electrodes upon opening the eyes, has been suggested as a potential marker of cholinergic system integrity.

METHODS

Eyes-open and eyes-closed resting state EEG data were recorded from 41 LBD patients (including 24 patients with DLB and 17 with PDD), 21 patients with AD, and 40 age-matched healthy controls. Alpha reactivity was calculated as the relative reduction in alpha power over occipital electrodes when opening the eyes. Structural MRI data were used to assess volumetric changes within the NBM using a probabilistic anatomical map.

RESULTS

Alpha reactivity was reduced in AD and LBD patients compared to controls with a significantly greater reduction in LBD compared to AD. Reduced alpha reactivity was associated with smaller volumes of the NBM across all groups (ρ = 0.42, pFDR = 0.0001) and in the PDD group specifically (ρ = 0.66, pFDR = 0.01).

CONCLUSIONS

We demonstrate that LBD patients show an impairment in alpha reactivity upon opening the eyes which distinguishes this form of dementia from AD. Furthermore, our results suggest that reduced alpha reactivity might be related to a loss of cholinergic drive from the NBM, specifically in PDD.

Electroencephalographic Workload Indicators During Teleoperation of an Unmanned Aerial Vehicle Shepherding a Swarm of Unmanned Ground Vehicles in Contested Environments

Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload.

Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system.

Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT).

Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR.

Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.

Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive-emotional training for treatment-resistant depression

Transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, is a promising treatment for depression. Recent research suggests that tDCS efficacy can be augmented using concurrent cognitive-emotional training (CET). However, the neurophysiological changes associated with this combined intervention remain to be elucidated. We therefore examined the effects of tDCS combined with CET using electroencephalography (EEG). A total of 20 participants with treatment-resistant depression took part in this open-label study and received 18 sessions over 6 weeks of tDCS and concurrent CET. Resting-state and task-related EEG during a 3-back working memory task were acquired at baseline and immediately following the treatment course. Results showed an improvement in mood and working memory accuracy, but not response time, following the intervention. We did not find significant effects of the intervention on resting-state power spectral density (frontal theta and alpha asymmetry), time-frequency power (alpha event-related desynchronisation and theta event-related synchronisation) or event-related potentials (P2 and P3 components). We therefore identified little evidence of neurophysiological changes associated with treatment using tDCS and concurrent CET, despite significant improvements in mood and near-transfer effects of cognitive training to working memory accuracy. Further research incorporating a sham-controlled group may be necessary to identify the neurophysiological effects of the intervention.

Prediction of working memory ability based on EEG by functional data analysis

BACKGROUND

There is always a demand for fast and accurate algorithms for EEG signal processing. Owing to the high sample rate, EEG signals usually come with a large number of sample points, making it difficult to predict the working memory ability in cognitive research with EEG.

NEW METHOD

Following well-designed experiments, the functional linear model provides a simple framework for regressions involving EEG signal predictors. The use of a data-driven basis in a linear structure naturally extends the standard linear regression model. The proposed approach utilizes B-spline approximation of functional principal components that greatly facilitates implementation.

RESULTS

Using LASSO feature selection, critical features have been extracted from eight frontal electrodes, and the R-square of 0.72 indicates rather strong linear association of actual observations and out-of-sample predictions.

COMPARISON WITH EXISTING METHODS

There does not seem to be any existing methods of predicting working memory ability from N-back task tests via EEG signals; the data-driven functional linear regression method proposed in this work is, to the best of our knowledge, the first of its kind.

CONCLUSIONS

The data analytics suggest that a multiple functional linear regression model for the predictive relationship between working memory ability and frontal activity of the brain is both feasible and accurate via EEG signal processing.

Acute effects of aerobic exercise on response variability and neuroelectric indices during a serial n-back task

To determine the neuroelectric underpinnings of exercise-induced changes in working memory, this study investigated the acute effects ofaerobic exercise (AE) on the P3 component of an event-related potential and brain oscillations during a serial n-back task. Task-related electroencephalography was collected in 23 young adults following 20 min of rest and AE on separate, counterbalanced days. The results revealed reductions in standard deviation of response time and coefficient of variation of response time following AE compared to rest. Neuroelectric analyses showed increased P3 amplitude following AE compared to rest. Task-related frontal alpha desynchronization was stronger in the 2-back compared with the 1-back task following AE, while no such modulation was observed following rest. These findings suggest AE may temporarily enhance working memory, as reflected by decreases in response variability, which are accompanied by neuroelectric indices reflecting greater upregulation of attentional processes.

The effects of visual distractors on cognitive load in a motor imagery brain-computer interface

A brain-computer interface (BCI) is a system that translates neural activity into a practical output. Its functionality, therefore, depends not only on the computer itself, but also on the cognitive system of the user. Distractors have the potential to capture attention, increase cognitive load, and may therefore impact BCI use. The purpose of the current study is to determine the effects of small visual distractors on the cognitive load of users of a motor imagery-BCI, and to examine whether these distractor-mediated effects can be improved by modifying the task interface. Sixteen typically-developed participants completed two sessions of online motor imagery to control an EEG-BCI, under conditions of no distractors, visual distractors, and cognitive strategies (intended to mitigate cognitive load) amid distractors. Cognitive load for each session was assessed through both a ratio of theta to alpha power and the NASA-Task Load Index (NASA-TLX). Task-irrelevant visual stimuli were found to significantly increase the objective measure of cognitive load, particularly for parietal channels. Subjective cognitive load as indexed by the NASA-TLX was predictive of a decrease in BCI performance for participants with below 0.75 classification accuracy (R² = 0.32, p < 0.001), which may indicate a differential susceptibility to changes in workload for "low"-performing participants. Quantifying and addressing the increased cognitive load imparted by distractors on BCI users can aid in the future applicability of the technology in real-world settings.

The Role of Physical Fitness in Cognitive-Related Biomarkers in Persons at Genetic Risk of Familial Alzheimer's Disease

Introduction: Nondemented people with a family history of Alzheimer’s disease (ADFH) and the ApoE-4 allele have been demonstrated to show a trend for a higher probability of cognitive decline and aberrant levels of cognitive-related biomarkers. However, the potential interactive effects on physical fitness have not been investigated. Purpose: The primary purpose of this study was to determine whether ADFH individuals with the ApoE-4 genotype show deviant brain event-related neural oscillatory performance and cognitively-related molecular indices. A secondary purpose was to examine the interactive effects on physical fitness. Methods: Blood samples were provided from 110 individuals with ADFH to assess molecular biomarkers and the ApoE genotype for the purpose of dividing them into an ApoE-4 group (n = 16) and a non-ApoE-4 group (n = 16) in order for them to complete a visuospatial working memory task while simultaneously recording electroencephalographic signals. They also performed a senior functional physical fitness (SFPF) test. Results: While performing the cognitive task, the ApoE-4 relative to non-ApoE-4 group showed worse accuracy rates (ARs) and brain neural oscillatory performance. There were no significant between-group differences with regard to any molecular biomarkers (e.g., IL-1β, IL-6, IL-8, BDNF, Aβ1-40, Aβ1-42). VO_2max was significantly correlated with the neuropsychological performance (i.e., ARs and RTs) in the 2-item and 4-item conditions in the ApoE-4 group and across the two groups. However, the electroencephalogram (EEG) oscillations during visuospatial working memory processing in the two conditions were not correlated with any SFPF scores or cardiorespiratory tests in the two groups. Conclusions: ADFH individuals with the ApoE-4 genotype only showed deviant neuropsychological (e.g., ARs) and neural oscillatory performance when performing the cognitive task with a higher visuospatial working memory load. Cardiorespiratory fitness potentially played an important role in neuropsychological impairment in this group.