Applications of EEG indices for the quantification of human cognitive performance: A systematic review and bibliometric analysis

Spatial Cognitive EEG Feature Extraction and Classification Based on MSSECNN and PCMI

With the aging population rising, the decline in spatial cognitive ability has become a critical issue affecting the quality of life among the elderly. Electroencephalogram (EEG) signal analysis presents substantial potential in spatial cognitive assessments. However, conventional methods struggle to effectively classify spatial cognitive states, particularly in tasks requiring multi-class discrimination of pre- and post-training cognitive states. This study proposes a novel approach for EEG signal classification, utilizing Permutation Conditional Mutual Information (PCMI) for feature extraction and a Multi-Scale Squeezed Excitation Convolutional Neural Network (MSSECNN) model for classification. Specifically, the MSSECNN classifies spatial cognitive states into two classes-before and after cognitive training-based on EEG features. First, the PCMI extracts nonlinear spatial features, generating spatial feature matrices across different channels. SENet then adaptively weights these features, highlighting key channels. Finally, the MSCNN model captures local and global features using convolution kernels of varying sizes, enhancing classification accuracy and robustness. This study systematically validates the model using cognitive training data from a brain-controlled car and manually operated UAV tasks, with cognitive state assessments performed through spatial cognition games combined with EEG signals. The experimental findings demonstrate that the proposed model significantly outperforms traditional methods, offering superior classification accuracy, robustness, and feature extraction capabilities. The MSSECNN model's advantages in spatial cognitive state classification provide valuable technical support for early identification and intervention in cognitive decline.

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Association Between Cognitive Function and the Autonomic Nervous System by Photoplethysmography

This study explored the relationship between cognitive function and the autonomic nervous system by categorizing participants into two groups based on their cognitive function scores in each domain of the SNSB-D: a High Cognitive Performance (HCP) group and a Low Cognitive Performance (LCP) group. We analyzed the Pulse Rate Variability (PRV) parameters for each group. Photoplethysmography (PPG) data were collected and processed to remove noise, and the PRV parameters in the time and frequency domains were extracted. To minimize the impact of age and years of education on the PRV parameters, we performed an adjusted analysis using a Generalized Linear Model (GLM). The analysis revealed that the autonomic nervous system, particularly the parasympathetic nervous system, was more activated in the LCP group compared to the HCP group. This finding suggests that in individuals with low cognitive function, the sympathetic nerves in the autonomic nervous system are less activated, so the parasympathetic nerves are relatively more activated. This study investigated the correlation between cognitive function and PRV parameters, highlighting the potential use of these parameters as indicators for the early diagnosis and classification of cognitive decline.

Systematic review of experimental paradigms and deep neural networks for electroencephalography-based cognitive workload detection

This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.

Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm

Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, this study successfully demonstrated the effectiveness of the paradigm. Results: Results indicate that both the beta/alpha ratio and EI are sensitive to changes in attentional demands, with beta/alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. Conclusions: The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to monitor attention fluctuations accurately and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining a high attention level is crucial.

The architecture of functional brain network modulated by driving under train running noise exposure

A noisy environment can considerably impact drivers' attention and fatigue, endangering driving safety. Consequently, this study designed a simulated driving experimental scenario to analyse the effects of noise generated during urban rail transit train operation on drivers' functional brain networks. The experiment recruited 16 participants, and the simulated driving scenario was conducted at noise levels of 50, 60, 70, and 80 dB. Functional connectivity between all electrode pairs across various frequency bands was evaluated using the weighted phase lag index (WPLI), and a brain network based on this was constructed. Graph theoretic analysis employed network global efficiency, degree, and clustering coefficient as metrics. Significant increases in the WPLI values of theta and alpha frequency bands were observed in high noise environments (70 dB, 80 dB), as well as enhanced brain synchronisation. Furthermore, concerning the topological metrics of brain networks, it was observed that the global efficiency of brain networks in theta and alpha frequency ranges, as well as the node degree and clustering coefficients, experienced substantial growth in high noise environments (70 dB, 80 dB) as opposed to 50 dB and 60 dB. This finding indicates that high-noise environments impact the reorganisation of functional brain networks, leading to a preference for network structures with improved global efficiency. Such findings may improve our understanding of the neural mechanisms of driving under noise exposure, and thus potentially reduce road accidents to some extent.

From E-Commerce to the Metaverse: A Neuroscientific Analysis of Digital Consumer Behavior

The growing interest in consumer behavior in the digital environment is leading scholars and companies to focus on consumer behavior and choices on digital platforms, such as the metaverse. On this immersive digital shopping platform, consumer neuroscience provides an optimal opportunity to explore consumers' emotions and cognitions. In this study, neuroscience techniques (EEG, SC, BVP) were used to compare emotional and cognitive aspects of shopping between metaverse and traditional e-commerce platforms. Participants were asked to purchase the same product once on a metaverse platform (Second Life, SL) and once via an e-commerce website (EC). After each task, questionnaires were administered to measure perceived enjoyment, informativeness, ease of use, cognitive effort, and flow. Statistical analyses were conducted to examine differences between SL and EC at the neurophysiological and self-report levels, as well as between different stages of the purchase process. The results show that SL elicits greater cognitive engagement than EC, but it is also more mentally demanding, with a higher workload and more memorization, and fails to elicit a strong positive emotional response, leading to a poorer shopping experience. These findings provide insights not only for digital-related consumer research but also for companies to improve their metaverse shopping experience. Before investing in the platform or creating a digital retail space, companies should thoroughly analyze it, focusing on how to enhance users' cognition and emotions, ultimately promoting a better consumer experience. Despite its limitations, this pilot study sheds light on the emotional and cognitive aspects of metaverse shopping and suggests potential for further research with a consumer neuroscience approach in the metaverse field.

Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG

The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.

Lavender Essential Oil Inhalation Improves Attentional Shifting and Accuracy: Evidence from Dynamic Changes of Cognitive Flexibility and Power Spectral Density of Electroencephalogram Signals

Background

Cognitive flexibility, a vital component of executive function, entails the utilization of extended brain networks. Olfactory stimulation has been shown to influence various brain functions, particularly cognitive performance.

Method

To investigate aroma inhalation's effects on brain activity dynamics associated with cognitive flexibility, 20 healthy adults were recruited to complete a set-shifting task during two experimental conditions: no aroma stimuli vs. lavender essential oil inhalation. Using Thomson's multitaper approach, the normalized power spectral density (NPSD) was assessed for five frequency bands.

Results

Findings confirm that aroma inhalation significantly affects behavioral indices (i.e., reaction time (RT) and response accuracy) and electroencephalogram (EEG) signatures, especially in the frontal lobe. Participants showed a tremendous increase in theta and alpha NPSD, associated with relaxation, along with beta NPSD, associated with clear and fast thinking after inhaling the aroma. NPSD of the delta band, an indicator of the unconscious mind, significantly decreased when stimulated with lavender essential oil. Further, participants exhibited shorter RT and more accurate responses following aroma inhalation.

Conclusion

Our findings revealed significant changes in oscillatory power and behavioral performance after aroma inhalation, providing neural evidence that olfactory stimulation with lavender essential oil may facilitate cognitive flexibility.

ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled "ADHD-AID". The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time-frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification's complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID's results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.

Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks

Introduction

The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments.

Methods

In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control.

Results

The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains.

Discussion

This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

The Impact of Mental Fatigue on a Strength Endurance Task: Is There a Role for the Movement-Related Cortical Potential?

PURPOSE

Several mechanisms have been proposed to explain how mental fatigue degrades sport performance. In terms of endurance performance, a role for an increased perceived exertion has been demonstrated. Using electroencephalography and, more specifically, the movement-related cortical potential (MRCP), the present study explored the neural mechanisms that could underlie the mental fatigue-associated increase in perceived exertion.

METHODS

Fourteen participants (age, 23 ± 2 yr; 5 women, 9 men) performed one familiarization and two experimental trials in a randomized, blinded, crossover study design. Participants had to complete a submaximal leg extension task after a mentally fatiguing task (EXP; individualized 60-min Stroop task) or control task (CON; documentary). The leg extension task consisted of performing 100 extensions at 35% of 1 repetition maximum, during which multiple physiological (heart rate, electroencephalography) and subjective measures (self-reported feeling of mental fatigue, cognitive load, behand motivation, ratings of perceived exertion) were assessed.

RESULTS

Self-reported feeling of mental fatigue was higher in EXP (72 ± 18) compared with CON (37 ± 17; P < 0.001). A significant decrease in flanker accuracy was detected only in EXP (from 0.96 ± 0.03% to 0.03%; P < 0.05). No significant differences between conditions were found in MRCP characteristics and perceived exertion. Specifically in EXP, alpha wave power increased during the leg extension task ( P < 0.01).

CONCLUSIONS

Mental fatigue did not impact the perceived exertion or MRCP characteristics during the leg extension task. This could be related to low perceived exertion and/or the absence of a performance outcome during the leg extension task. The increase in alpha power during the leg extension task in EXP suggests that participants may engage a focused internal attention mechanism to maintain performance and mitigate feelings of fatigue.

Real-time estimation of EEG-based engagement in different tasks

Objective.Recent trends in brain-computer interface (BCI) research concern the passive monitoring of brain activity, which aim to monitor a wide variety of cognitive states. Engagement is such a cognitive state, which is of interest in contexts such as learning, entertainment or rehabilitation. This study proposes a novel approach for real-time estimation of engagement during different tasks using electroencephalography (EEG).Approach.Twenty-three healthy subjects participated in the BCI experiment. A modified version of the d2 test was used to elicit engagement. Within-subject classification models which discriminate between engaging and resting states were trained based on EEG recorded during a d2 test based paradigm. The EEG was recorded using eight electrodes and the classification model was based on filter-bank common spatial patterns and a linear discriminant analysis. The classification models were evaluated in cross-task applications, namely when playing Tetris at different speeds (i.e. slow, medium, fast) and when watching two videos (i.e. advertisement and landscape video). Additionally, subjects' perceived engagement was quantified using a questionnaire.Main results.The models achieved a classification accuracy of 90% on average when tested on an independent d2 test paradigm recording. Subjects' perceived and estimated engagement were found to be greater during the advertisement compared to the landscape video (p= 0.025 andp<0.001, respectively); greater during medium and fast compared to slow Tetris speed (p<0.001, respectively); not different between medium and fast Tetris speeds. Additionally, a common linear relationship was observed for perceived and estimated engagement (rrm= 0.44,p<0.001). Finally, theta and alpha band powers were investigated, which respectively increased and decreased during more engaging states.Significance.This study proposes a task-specific EEG engagement estimation model with cross-task capabilities, offering a framework for real-world applications.

Developmental Dyslexia: Insights from EEG-Based Findings and Molecular Signatures-A Pilot Study

Developmental dyslexia (DD) is a learning disorder. Although risk genes have been identified, environmental factors, and particularly stress arising from constant difficulties, have been associated with the occurrence of DD by affecting brain plasticity and function, especially during critical neurodevelopmental stages. In this work, electroencephalogram (EEG) findings were coupled with the genetic and epigenetic molecular signatures of individuals with DD and matched controls. Specifically, we investigated the genetic and epigenetic correlates of key stress-associated genes (NR3C1, NR3C2, FKBP5, GILZ, SLC6A4) with psychological characteristics (depression, anxiety, and stress) often included in DD diagnostic criteria, as well as with brain EEG findings. We paired the observed brain rhythms with the expression levels of stress-related genes, investigated the epigenetic profile of the stress regulator glucocorticoid receptor (GR) and correlated such indices with demographic findings. This study presents a new interdisciplinary approach and findings that support the idea that stress, attributed to the demands of the school environment, may act as a contributing factor in the occurrence of the DD phenotype.

Investigation of different ML approaches in classification of emotions induced by acute stress

Background

Machine learning is becoming a common tool in monitoring emotion. However, methodological studies of the processing pipeline are scarce, especially ones using subjective appraisals as ground truth.

New method

A novel protocol was used to induce cognitive load and physical discomfort, and emotional dimensions (arousal, valence, and dominance) were reported after each task. The performance of five common ML models with a versatile set of features (physiological features, task performance data, and personality trait) was compared in binary classification of subjectively assessed emotions.

Results

The psychophysiological responses proved the protocol was successful in changing the mental state from baseline, also the cognitive and physical tasks were different. The optimization and performance of ML models used for emotion detection were evaluated. Additionally, methods to account for imbalanced classes were applied and shown to improve the classification performance.

Comparison with existing methods

Classification of human emotional states often assumes the states are determined by the stimuli. However, individual appraisals vary. None of the past studies have classified subjective emotional dimensions with a set of features including biosignals, personality and behavior.

Conclusion

Our data represent a typical setup in affective computing utilizing psychophysiological monitoring: N is low compared to number of features, inter-individual variability is high, and class imbalance cannot be avoided. Our observations are a) if possible, include features representing physiology, behavior and personality, b) use simple models and limited number of features to improve interpretability, c) address the possible imbalance, d) if the data size allows, use nested cross-validation.

The Evolution of Neuroimaging Technologies to Evaluate Neural Activity Related to Knee Pain and Injury Risk

PURPOSE OF REVIEW

In this review, we present recent findings and advancements in the use of neuroimaging to evaluate neural activity relative to ACL injury risk and patellofemoral pain. In particular, we describe prior work using fMRI and EEG that demonstrate the value of these techniques as well as the necessity of continued development in this area. Our goal is to support future work by providing guidance for the successful application of neuroimaging techniques that most effectively expose pain and injury mechanisms.

RECENT FINDINGS

Recent studies that utilized both fMRI and EEG indicate that athletes who are at risk for future ACL injury exhibit divergent brain activity both during active lower extremity movement and at rest. Such activity patterns are likely due to alterations to cognitive, visual, and attentional processes that manifest as coordination deficits during naturalistic movement that may result in higher risk of injury. Similarly, in individuals with PFP altered brain activity in a number of key regions is related to subjective pain judgements as well as measures of fear of movement. Although these findings may begin to allow objective pain assessment and identification, continued refinement is needed. One key limitation across both ACL and PFP related work is the restriction of movement during fMRI and EEG data collection, which drastically limits ecological validity. Given the lack of sufficient research using EEG and fMRI within a naturalistic setting, our recommendation is that researchers target the use of mobile, source localized EEG as a primary methodology for exposing neural mechanisms of ACL injury risk and PFP. Our contention is that this method provides an optimal balance of spatial and temporal resolution with ecological validity via naturalistic movement.

The role of clinical neurophysiology in the definition and assessment of fatigue and fatigability

Though a common symptom, fatigue is difficult to define and investigate, occurs in a wide variety of neurological and systemic disorders, with differing pathological causes. It is also often accompanied by a psychological component. As a symptom of long-term COVID-19 it has gained more attention. In this review, we begin by differentiating fatigue, a perception, from fatigability, quantifiable through biomarkers. Central and peripheral nervous system and muscle disorders associated with these are summarised. We provide a comprehensive and objective framework to help identify potential causes of fatigue and fatigability in a given disease condition. It also considers the effectiveness of neurophysiological tests as objective biomarkers for its assessment. Among these, twitch interpolation, motor cortex stimulation, electroencephalography and magnetencephalography, and readiness potentials will be described for the assessment of central fatigability, and surface and needle electromyography (EMG), single fibre EMG and nerve conduction studies for the assessment of peripheral fatigability. The purpose of this review is to guide clinicians in how to approach fatigue, and fatigability, and to suggest that neurophysiological tests may allow an understanding of their origin and interactions. In this way, their differing types and origins, and hence their possible differing treatments, may also be defined more clearly.

One hundred years of neurosciences in the arts and humanities, a bibliometric review

BACKGROUND

Neuroscientific approaches have historically triggered changes in the conception of creativity and artistic experience, which can be revealed by noting the intersection of these fields of study in terms of variables such as global trends, methodologies, objects of study, or application of new technologies; however, these neuroscientific approaches are still often considered as disciplines detached from the arts and humanities. In this light, the question arises as to what evidence the history of neurotechnologies provides at the intersection of creativity and aesthetic experience.

METHODS

We conducted a century-long bibliometric analysis of key parameters in multidisciplinary studies published in the Scopus database. Screening techniques based on the PRISMA method and advanced data analysis techniques were applied to 3612 documents metadata from the years 1922 to 2022. We made graphical representations of the results applying algorithmic and clusterization processes to keywords and authors relationships.

RESULTS

From the analyses, we found a) a shift from a personality-focus quantitative analysis to a field-focus qualitative approach, considering topics such as art, perception, aesthetics and beauty; b) The locus of interest in fMRI-supported neuroanatomy has been shifting toward EEG technologies and models based on machine learning and deep learning in recent years; c) four main clusters were identified in the study approaches: humanistic, creative, neuroaesthetic and medical; d) the neuroaesthetics cluster is the most central and relevant, mediating between creativity and neuroscience; e) neuroaesthetics and neuroethics are two of the neologism that better characterizes the challenges that this convergence of studies will have in the next years.

CONCLUSIONS

Through a longitudinal analysis, we evidenced the great influence that neuroscience is having on the thematic direction of the arts and humanities. The perspective presented shows how this field is being consolidated and helps to define it as a new opportunity of great potential for future researchers.

Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.

Event-related potentials for investigating the willingness to recycle household medical waste

Household medical waste (HMW) recycling in the reverse supply chain has become a primary channel for infectious, toxic, or radioactive substances for environmental protection and a circular economy. Recycling managers need to understand the recycling decision-making mechanisms of households to improve the intention-behavior gap and recycling participation rate, especially in cognitive neuroscience. This study designed an event-related potential (ERPs) experiment to explore the differences in ERPs components between the willingness and unwillingness to make recycling decisions. Our findings confirmed that willingness and unwillingness to recycle can lead to a significant difference in the P300 and N400 scores. A larger P300 was evoked by willingness rather than unwillingness in the prefrontal, frontal, and frontal-temporal regions. This indicates that willingness to recycle results from a rational choice in the decision-making process. However, a larger N400 was evoked by unwillingness rather than willingness in the parietal, parietal-occipital, and occipital regions. A negative wave was evoked in households unwilling to recycle because they thought it was dangerous and unsanitary, causing a higher conflict with intrinsic cognition. The combination of HMW recycling decisions and neurology may accurately measure pro-environmental decision-making processes through brain science. Advancing the knowledge of psychological and brain mechanism activities for understanding pro-environmental choices. In turn, this can help recycling managers to accurately understand household demands for increasing the recycling intention and designing effective HMW take-back systems to solve the intention-behavior gap related to the global recycling dilemma.

Heart-brain Rhythmic Synchronization during Meditation: A Nonlinear Signal Analysis

Background

Heart-brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart-brain synchronization with respect to mental and emotional health and well-being. The current investigation aims to explore the rhythmic synchronicity between the brain and the heart during heartfulness meditation (HM) practice.

Materials and Methods

The study was performed on 45 healthy volunteers who were categorized into three equal groups: long-term meditators (LTMs), short-term meditators (STMs), and nonmeditators (NMs). The electroencephalogram (EEG) signals were recorded to measure the prefrontal activity, and electrocardiogram (ECG) signals were recorded to measure the cardiac activity. The data were recorded in four states: baseline, meditation, transmission, and posttransmission. The detrended fluctuation analysis (DFA) method was used for the analysis of EEG and ECG signals.

Results

The result indicates that DFA values of EEG and ECG declined during meditation and transmission states as compared to pre- and postmeditation states. Significant results were obtained for the LTM group in all the states. A positive correlation was also observed between DFA of the heart and brain for the LTM group and no significant correlations were observed for the STM and NM groups.

Conclusion

The shreds of evidence suggest that heart-brain synchronization facilitates mental and emotional stability. HM practice has the potential to regulate the fluctuation of the mind. Regular meditation practice may result in physiological synchrony between cardiac and neural behavior, which can be considered a quality index for meditation practice.

Impact of swiping direction on the interaction performance of elderly-oriented smart home interface: EEG and eye-tracking evidence

Introduction

Smart home technology is increasingly popular, yet not all seniors are receptive and comfortable with it. This situation recognizes that the usability of smart home interfaces is particularly important. Most studies on interface swiping direction demonstrate the advantages of horizontal over vertical swiping, but the findings lack age-based as well as gender-specific judgments.

Methods

In this paper, we use cognitive neural techniques of EEG and eye-tracking, combined with a subjective preference questionnaire, to analyze the preference of older persons for the swiping direction of smart home interfaces in a multimodal manner.

Results

The EEG data showed that swiping direction had a significant effect on potential values (p = 0.001). Also, the mean power in the δ and the θ band was enhanced during vertical swiping. Gender had no significant effect on potential values (p = 0.085), but the cognitive task was more EEG stimulating for females. The eye-tracking metrics data showed a significant effect of swiping direction on fixation duration (p = 0.047) and a non-significant effect on pupil diameter (p = 0.576). These results were consistent with the results of the subjective preference questionnaire, both demonstrating a preference for vertical swiping among participants.

Discussion

This paper uses three research tools simultaneously, combining objective perceptions as well as subjective preferences, to make the findings more comprehensive and reliable. Gender differences were also taken into account and differentiated in the data processing. The findings of this paper are different from most previous studies and better reflect the preference of elderly people for swiping directions, which can provide a reference for the future elderly-friendly smart home interface design.

Multiple functions of the angular gyrus at high temporal resolution

Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800 ms in a wide range of domains, with a high probability to detect an effect at 300-350 ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200 ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500 ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.

EEG-based analysis of various sensory stimulation effects to reduce visually induced motion sickness in virtual reality

The use of virtual reality (VR) is frequently accompanied by motion sickness, and approaches for preventing it are not yet well established. We explored the effects of synchronized presentations of sound and motion on visually induced motion sickness (VIMS) in order to reduce VIMS. A total of 25 participants bicycle riding for 5 min with or without sound and motion synchronization presented on a head-mounted display. As a result, the VIMS scores measured by the fast motion sickness scale and simulator sickness questionnaire were significantly lower in the participants who experienced the riding scene with sound and motion than those who experienced the riding scene with sound only, motion only, or neither. Furthermore, analysis of the EEG signal showed that the higher the VIMS, the significant increase in alpha and theta waves in the parietal and occipital lobes. Therefore, we demonstrate that the simultaneous presentation of sound and motion, closely associated with synchronous and visual flow speed, is effective in reducing VIMS while experiencing simulated bicycle riding in a VR environment.

Effects of Olfactory Stimulation with Aroma Oils on Psychophysiological Responses of Female Adults

This study investigated the effects of olfactory stimulation with aroma oils on the psychophysiological responses in women. Ten aromatic oils (lavender, rosemary, rose, eucalyptus, jasmine, geranium, chamomile, clary sage, thyme, and peppermint) were used on 23 women aged between 20 and 60 years. They inhaled the scent for 90 s through a glass funnel attached to their lab apron, 10 cm below their nose, while the pump was activated. Electroencephalography, blood pressure, and pulse rate were measured before and during inhalation of the aroma oils. The relative alpha (RA) power spectrums indicating relaxation and resting state of the brain significantly increased when lavender, rosemary, eucalyptus, jasmine, chamomile, clary sage, and thyme oils were inhaled compared to those of before olfactory stimulation. The ratio of alpha to high beta (RAHB), an indicator of brain stability and relaxation, significantly increased when rosemary, jasmine, clary sage, and peppermint oils were inhaled. The relative low beta (RLB) power spectrum, an indicator of brain activity in the absence of stress, significantly increased when stimulated with lavender, rosemary, rose, and geranium scents. Further, systolic blood pressure significantly decreased after introduction of all 10 types of aromatic oils, which indicates stress reduction. Thus, olfactory stimulation with aroma oil had a stabilizing effect on the prefrontal cortex and brain activity and decreased systolic blood pressure.

Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study

Autism Spectrum Disorder (ASD) is a neurodevelopmental life condition characterized by problems with social interaction, low verbal and non-verbal communication skills, and repetitive and restricted behavior. People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. Attention is a process that occurs at the cognitive level and allows us to orient ourselves towards relevant stimuli, ignoring those that are not, and act accordingly. This paper presents a methodology based on electroencephalographic (EEG) signals for attention measurement in a 13-year-old boy diagnosed with ASD. The EEG signals are acquired with an Epoc+ Brain–Computer Interface (BCI) via the Emotiv Pro platform while developing several learning activities and using Matlab 2019a for signal processing. For this article, we propose to use electrodes F3, F4, P7, and P8. Then, we calculate the band power spectrum density to detect the Theta Relative Power (TRP), Alpha Relative Power (ARP), Beta Relative Power (BRP), Theta–Beta Ratio (TBR), Theta–Alpha Ratio (TAR), and Theta/(Alpha+Beta), which are features related to attention detection and neurofeedback. We train and evaluate several machine learning (ML) models with these features. In this study, the multi-layer perceptron neural network model (MLP-NN) has the best performance, with an AUC of 0.9299, Cohen’s Kappa coefficient of 0.8597, Matthews correlation coefficient of 0.8602, and Hamming loss of 0.0701. These findings make it possible to develop better learning scenarios according to the person’s needs with ASD. Moreover, it makes it possible to obtain quantifiable information on their progress to reinforce the perception of the teacher or therapist.

Neuronal Abnormalities Induced by an Intelligent Virtual Reality System for Methamphetamine Use Disorder

Methamphetamine use disorder (MUD) is a brain disease that leads to altered regional neuronal activity. Virtual reality (VR) is used to induce the drug cue reactivity. Previous studies reported significant frequency-specific abnormalities in patients with MUD during VR induction of drug craving. However, whether those patients exhibit neuronal abnormalities after VR induction that could serve as the treatment target remains unclear. Here, we developed an integrated VR system for inducing drug related changes and investigated the neuronal abnormalities after VR exposure in patients. Fifteen patients with MUD and ten healthy subjects were recruited and exposed to drug-related VR environments. Resting-state EEG were recorded for 5 minutes twice-before and after VR and transformed to obtain the frequency-specific data. Three self-reported scales for measurement of the anxiety levels and impulsivity of participants were obtained after VR task. Statistical tests and machine learning methods were employed to reveal the differences between patients and healthy subjects. The result showed that patients with MUD and healthy subjects significantly differed in, and power changes after VR. These neuronal abnormalities in patients were associated with the self-reported behavioral scales, indicating impaired impulse control. Our findings of resting-state EEG abnormalities in patients with MUD after VR exposure have the translation value and can be used to develop the treatment strategies for methamphetamine use disorder.

Mirror Therapy Rehabilitation in Stroke: A Scoping Review of Upper Limb Recovery and Brain Activities

BACKGROUND

Mirror therapy (MT) has been used as a treatment for various neurological disorders. Recent application of electroencephalogram (EEG) to the MT study allows researchers to gain insight into the changes in brain activity during the therapy.

OBJECTIVE

This scoping review is aimed at mapping existing evidence and identifying knowledge gaps about the effects of MT on upper limb recovery and its application for individuals with chronic stroke.

METHODS AND MATERIALS

A scoping review through a systematic literature search was conducted using PubMed, CINAHL, PsycINFO, and Scopus databases. Twenty articles published between 2010 and 2020 met the inclusion criteria. The efficacy of MT on upper limb recovery and brain activity during MT were discussed according to the International Classification of Functioning, Disability and Health (ICF).

RESULTS

A majority of the studies indicated positive effects of MT on upper limb recovery from the body structure/functional domain. All studies used EEG to indicate brain activation during MT.

CONCLUSION

MT is a promising intervention for improving upper limb function for individuals with chronic stroke. This review also highlights the need to incorporate EEG into the MT study to capture brain activity and understand the mechanism underlying the therapy.

Mental Fatigue-Associated Decrease in Table Tennis Performance: Is There an Electrophysiological Signature?

Mental fatigue (MF) is a psychobiological state negatively impacting both cognitive and physical performance. Although recent research implies that some table tennis (TT) performance outcomes are impaired by MF, open skill sports such as TT require a more detailed overview of MF-related performance decrements. Moreover, research into MF and sport-specific psychomotor performance lacks the inclusion of brain-related measurements to identify MF mechanisms. Eleven experienced TT players participated in this randomized counterbalanced crossover trial. Participants were either required to perform an individualized Stroop task (MF condition) or watch a documentary (control condition). The primary outcomes were reaction time on a sport-specific visuomotor task and EEG activity throughout the trial. The subjective feeling of MF was significantly different between both conditions and confirmed that the MF condition induced the mentally fatigue state of participants (p < 0.001), though no behavioral indicators (i.e., decrease in performance on Stroop and flanker task) of MF. MF worsened reaction time on the visuomotor task, while other secondary measurements remained largely ambiguous. Spectral power (i.e., decreases in upper α band and θ band) was influenced by MF, while ERPs measured during the visuomotor task remained unaltered. The present study confirms that MF negatively impacts table tennis performance, specifically inhibitory stimuli during the visuomotor task. These findings also further augment our understanding of the effects of MF on human performance.

Mismatch of Visual-Vestibular Information in Virtual Reality: Is Motion Sickness Part of the Brains Attempt to Reduce the Prediction Error?

Visually induced motion sickness (VIMS) is a relevant limiting factor in the use of virtual reality (VR) devices. Understanding the origin of this problem might help to develop strategies to circumvent this limitation. Previous studies have attributed VIMS to a mismatch between visual, and vestibular information, causing ambiguity of the position of the body in relation to its surrounding. Studies using EEG have shown a shift of the power spectrum to lower frequencies while VIMS is experienced. However, little is known about the relationship between the intensity of the VIMS and the changes in these power spectra. Moreover, the effect of different varieties of VIMS on the causal relationship between brain areas is largely unknown. Here, we used EEG to study 14 healthy subjects in a VR environment who were exposed to increasing levels of mismatch between vestibular and visual information. The frequency power and the bivariate transfer entropy as a measure for the information transfer were calculated. We found a direct association between increasing mismatch levels and subjective VIMS. With increasing VIMS, the proportion of slow EEG waves (especially 1–10 Hz) increases, especially in temporo-occipital regions. Furthermore, we found a general decrease in the information flow in most brain areas but especially in brain areas involved in the processing of vestibular signals and the detection of self-motion. We hypothesize that the general shift of frequency power and the decrease in information flow while experiencing high intensity VIMS represent a brain state of a reduced ability to receive, transmit and process information. We further hypothesize that the mechanism of reduced information flow is a general reaction of the brain to an unresolvable mismatch of information. This reaction aims on transforming a currently unstable model with a high prediction error into a stable model in an environment of minimal contradictory information.

Tracking of Mental Workload with a Mobile EEG Sensor

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

Effect of Fragrant Primula Flowers on Physiology and Psychology in Female College Students: An Empirical Study

Indoor plants can positively impact physical and mental health in daily life. However, the benefits of viewing indoor plants may be enhanced if the plants emit a fragrant aroma. In this crossover-design study, we measured the physiological and psychological effects of fragrant and non-fragrant Primula plants on 50 female college students, and explored whether aroma stimulation had additive benefits for this group. Non-fragrant Primula malacoides Franch was used as a control stimulus, and Primula forbesii Franch, which has a floral fragrance, was used as an experimental stimulus. We measured blood pressure, pulse rate, and electroencephalogram (EEG) to evaluate physiological responses, and used a mood state profile and the semantic differential (SD) method to evaluate psychological responses. We found that mean blood pressure and pulse rate decreased significantly after the experiment in both conditions. EEGs showed that the mean values of high alpha waves, high beta waves, and relaxation scores were significantly higher in the experimental vs. control condition. The average scores on each subscale of the psychological questionnaire improved after the experiment in both conditions, and the vitality (V) subscale and total emotional state scores were significantly better in the experimental vs. control condition. The results of the SD method showed that the sense of relaxation and comfort were significantly higher in the experimental vs. control condition. Compared with the non-fragrant Primula, the fragrant Primula induced relatively better physiological and psychological effects.