fNIRS Evidence for Recognizably Different Positive Emotions

Do you feel betrayed? Exploring the impact of workplace-induced loneliness on interactions with varied social structures

BACKGROUND

Workplace loneliness is an escalating concern, affecting employee well-being and productivity. Understanding its impact on social interactions and decision-making within professional settings is crucial for developing effective interventions.

OBJECTIVE

This study aims to explore how workplace-induced loneliness influences individuals' interactions with social groups, individuals, and computer programs, and to assess the behavioral, cognitive, and emotional outcomes of these interactions. To explain these observed phenomena, the Workplace Loneliness-Driven Social Response (WL-SR) model is proposed.

METHODS

A dark factory decision-making experiment was designed and conducted, where participants underwent loneliness induction before engaging in tasks that required interactions with different social structures. The study measured changes in trust, emotional responses, neural activities, and decision-making processes to evaluate the impact of loneliness.

RESULTS

The findings indicate that loneliness significantly increases distrust and dishonesty in interactions with social groups, leading to higher dissatisfaction and negative emotional responses. Conversely, interactions with a social individual were marked by increased reliability and more positive attributions, which mitigated feelings of loneliness. The WL-SR model, integrating stress-related fight-or-flight and tend-and-befriend responses, elucidates these outcomes.

CONCLUSIONS

This study reveals how workplace loneliness affects trust and social interactions in professional settings. It highlights the negative impact on group interactions and the potential for individual interactions to reduce loneliness. The findings contribute to the understanding of how human psychology interacts with digital communication in the workplace, emphasizing the role of computers in mediating responses to loneliness.

Altered cortical activation patterns in post-stroke patients during walking with two-channel functional electrical stimulation: a functional near-infrared spectroscopy observational study

Restoration of independent walking ability is the primary objective of stroke rehabilitation; however, not all patients achieve this goal due to diverse impairments in the paretic lower limb and compensatory mechanisms that lead to an asymmetrical and mechanically inefficient gait. This investigation aimed to examine alterations in cortical activation in post-stroke patients while walking with a wearable two-channel functional electrical stimulation (FES) in comparison to walking without FES. This observational study was conducted to discern distinct activation patterns in 19 stroke patients during sessions with and without FES, while using functional near-infrared spectroscopy (fNIRS) to monitor changes in blood oxygen levels. Our findings revealed only a significant reduction in ΔOxy-Hb in the contralesional pre-motor cortex (z = -2.803, p = 0.005) during the FES-on walking sessions compared to the FES-off sessions. Furthermore, all regions in the FES-on session exhibited lower ΔOxy-Hb. Conversely, no significant differences were observed in ΔDeoxy-Hb. Moreover, a significant correlation was found between decrease in cPMC and the reduced cost time of walking under FES-on condition. The fNIRS analysis revealed diminished activation in the contralesional pre-motor cortex when walking with FES, implying that FES may facilitate a more automatic gait pattern while reducing a patient's reliance on contralesional cortical resources. The findings of this study lay the groundwork for long-term neural rehabilitation.

Age-related cerebral changes during different n-back tasks: a functional near-infrared spectroscopy study

Background

The n-back task is a widely used paradigm to assess working memory and is commonly applied in research on age-related cognitive decline. However, studies utilizing functional near-infrared spectroscopy (fNIRS) to explore this area are limited.

Objective

This study aims to investigate age-related differences in brain activation during the n-back task using fNIRS.

Methods

fNIRS data were collected from 18 elderly and 19 young participants while performing different n-back tasks. Brain activation patterns and peripheral performance were compared between the two groups.

Results

Significant differences in brain activation patterns were observed between elderly and young participants. Under the 3-back condition, the older group exhibited reduced activation in brain regions adjacent to prefrontal cognitive areas compared to the younger group. Additionally, the older group's performance plateaued at the 2-back level, along with a decline in prefrontal activation.

Conclusion

These findings may suggest potential markers for cognitive decline, providing a new target for future screening.

Machine-Learning-Based Prediction of Photobiomodulation Effects on Older Adults With Cognitive Decline Using Functional Near-Infrared Spectroscopy

Transcranial photobiomodulation (tPBM) has been widely studied for its potential to enhance cognitive functions of the elderly. However, its efficacy varies, with some individuals exhibiting no significant response to the treatment. Considering these inconsistencies, we introduce a machine learning approach aimed at distinguishing between individuals that respond and do not respond to tPBM treatment based on functional near-infrared spectroscopy (fNIRS) acquired before the treatment. We measured nine cognitive scores and recorded fNIRS data from 62 older adults with cognitive decline (43 experimental and 19 control subjects). The experimental group underwent tPBM intervention over a span of 12 weeks. Based on the comparison of the global cognitive score (GCS), merging the nine cognitive scores into a single representation, acquired before and after tPBM treatment, we classified all participants as responders or non-responders to tPBM with a threshold for the GCS change. The fNIRS data were recorded during the resting state, recognition memory task (RMT), Stroop task, and verbal fluency task. A regularized support vector machine was utilized to classify the responders and non-responders to tPBM. The most promising performance of our machine learning model was observed when using the fNIRS data collected during the RMT, which yielded an accuracy of 0.8537, an F1-score of 0.8421, sensitivity of 0.7619, and specificity of 0.95. To the best of our knowledge, this is the first study to demonstrate the feasibility of predicting the tPBM efficacy. Our approach is expected to contribute to more efficient treatment planning by excluding ineffective treatment options.

Early screening of post-stroke fall risk: A simultaneous multimodal fNIRs-EMG study

BACKGROUND

Stroke is the third-leading cause of death and disability, and poststroke falls (PSF) are common at all stages after stroke and could even lead to injuries or death. Brain information from functional near-infrared spectroscopy (fNIRs) may precede conventional imaging and clinical symptoms but has not been systematically considered in PSF risk prediction. This study investigated the difference in brain activation between stroke patients and healthy subjects, and this study was aimed to explore fNIRs biomarkers for early screening of PSF risk by comparing the brain activation in patients at and not at PSF risk.

METHODS

In this study, we explored the differences in brain activation and connectivity between stroke and healthy subjects by synchronizing the detection of fNIRs and EMG tests during simple (usual sit-to-stand) and difficult tasks (sit-to-stand based on EMG feedback). Thereby further screened for neuroimaging biomarkers for early prediction of PSF risk by comparing brain activation variability in poststroke patients at and not at fall risk during simple and difficult tasks. The area under the ROC curve (AUROC), sensitivity, and specificity were used to compare the diagnostic effect.

RESULTS

A total of 40 patients (22 not at and 18 at PSF risk) and 38 healthy subjects were enrolled. As the difficulty of standing task increased, stroke patients compared with healthy subjects further increased the activation of the unaffected side of supplementary motor area (H-SMA) and dorsolateral prefrontal cortex-Brodmann area 46 (H-DLFC-BA46) but were unable to increase functional connectivity (Group*Task: p < 0.05). More importantly, the novel finding showed that hyperactivation of the H-SMA during a simple standing task was a valid fNIRs predictor of PSF risk [AUROC 0.74, p = 0.010, sensitivity 77.8%, specificity 63.6%].

CONCLUSIONS

This study provided novel evidence that fNIR-derived biomarkers could early predict PSF risk that can facilitate the widespread use of real-time assessment tools in early screening and rehabilitation. Meanwhile, this study demonstrated that the higher brain activation and inability to increase the brain functional connectivity in stroke patients during difficult task indicated the inefficient use of brain resources.

EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person's emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG-fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG-fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG-fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG-fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3-11%.

Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research

Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.

Interruption Value Type and Source Matter in Different Phases of an Interruption Process: Emotional/Cognitive Impact and Neural Evidence

OBJECTIVE

To examine the effect of interruption value type (utilitarian, hedonic) and source (external, internal) in different phases of an interruption process.

BACKGROUND

Prior studies on interruption mostly focused on the cognitive outcomes of utilitarian interruptions. Hedonic interruptions are common in life; however, they are not sufficiently explored through research. Hedonic value is closely associated with emotional experiences, and, in turn, influences behaviors. Moreover, the way the effect of values is moderated by interruptions initiated by intrinsic motives is worth exploring.

METHOD

A 2 × 2 mixed design experiment was conducted with 48 participants who were asked to respond to instant messages during the writing task. The interruption value was induced by work or non-work tasks. The interruption source was manipulated by providing an alert. Functional near-infrared spectroscopy, behavioral data, and subjective measurements were collected and analyzed.

RESULTS

Hedonic interruptions increased emotional valence, while utilitarian interruptions decreased it. These effects were strengthened by internal interruptions. The effect of interruption value type on work exhaustion was also moderated by the source. Interruption value type and source influenced the attention allocation before an interruption occurred. Hedonic interruptions led to longer resumption lags, whereas utilitarian interruptions required longer interruption durations. Internal interruptions led to improved performance in the resumed task.

CONCLUSION

Interruption source modulate the effect of interruption value type, especially on emotional experience and attention allocation before an interruption occurs.

APPLICATION

Self-initiated hedonic interruptions have emotional benefits, while alerts for utilitarian interruptions will improve attention on the main task before interruptions.

Evaluation of the learning state of online video courses based on functional near infrared spectroscopy

Studying brain activity during online learning will help to improve research on brain function based on real online learning situations, and will also promote the scientific evaluation of online education. Existing research focuses on enhancing learning effects and evaluating the learning process associated with online learning from an attentional perspective. We aimed to comparatively analyze the differences in prefrontal cortex (PFC) activity during resting, studying, and question-answering states in online learning and to establish a classification model of the learning state that would be useful for the evaluation of online learning. Nineteen university students performed experiments using functional near-infrared spectroscopy (fNIRS) to monitor the prefrontal lobes. The resting time at the start of the experiment was the resting state, watching 13 videos was the learning state, and answering questions after the video was the answering state. Differences in student activity between these three states were analyzed using a general linear model, 1s fNIRS data clips, and features, including averages from the three states, were classified using machine learning classification models such as support vector machines and k-nearest neighbor. The results show that the resting state is more active than learning in the dorsolateral prefrontal cortex, while answering questions is the most active of the three states in the entire PFC, and k-nearest neighbor achieves 98.5% classification accuracy for 1s fNIRS data. The results clarify the differences in PFC activity between resting, learning, and question-answering states in online learning scenarios and support the feasibility of developing an online learning assessment system using fNIRS and machine learning techniques.

Comparison of different rhythmic auditory stimuli on prefrontal cortex cortical activation during upper limb movement in patients with Parkinson's disease: a functional near-infrared spectroscopy study

Background

A large number of literatures show that rhythmic auditory stimulation (RAS) can effectively improve Parkinson's disease (PD) patients' gait speed, frequency and speed. Its application and curative effect on upper limb motor function is relatively few.

Objective

By studying the immediate effect of RAS with different rhythms on the prefrontal cortex (PFC) blood oxygen response during upper limb movement in PD patients, this study discusses the potential neurophysiological mechanism of RAS on upper limb movement in PD patients, which is expected to provide guidance for patients with upper limb dysfunction such as Parkinson's disease.

Methods

In this study, 31 PD patients with upper limb static tremors were recruited to complete the nail board task on the healthy upper limb under the baseline rhythm, slow rhythm and fast rhythm provided by the therapist. At the same time, fNIRS was used to observe the blood oxygen response of PFC.

Results

There was no significant main effect onsidein all brain regions (p > 0.05), and there was no interaction between rhythm and side (p > 0.05); Except lPFC, the main effect of rhythm in other brain regions was significant (p < 0.05), and ΔHbO increased with the change of rhythm. Paired analysis showed that there were significant differences in ΔHbO between slow rhythm and baseline rhythm, between fast rhythm and baseline rhythm, and between slow rhythm and fast rhythm (p < 0.05); The ΔHbO of rPFC, lDLPFC and rDLPFC were significantly different between slow rhythm and fast rhythm (p < 0.05); there were significant differences in the ΔHbO of BA8 between slow rhythm and baseline rhythm, and between slow rhythm and fast rhythm (p < 0.05).

Conclusion

RAS may be a useful upper limb rehabilitation strategy for PD patients with upper limb dysfunction. At the same time, RAS with different rhythms also have different responses to PFC blood oxygen during upper limb movement in PD patients, so that we can design interventions for this kind of cortical mechanism. Identifying the neurophysiological mechanism of RAS on upper limb movement in PD patients may help clinicians customize rehabilitation methods for patients according to clues, so as to highly personalize upper limb training and optimize its effect.

An extended emotion-eliciting film clips set (EGEFILM): assessment of emotion ratings for 104 film clips in a Turkish sample

The primary aim of this study was to test emotion-elicitation levels of widely used film clips in a Turkish sample and to expand existing databases by adding several new film clips with the capacity to elicit a wide range of emotions, including a rarely studied emotion category, i.e., calmness. For this purpose, we conducted a comprehensive review of prior studies and collected a large number of new suggestions from a Turkish sample to select film clips for eight emotion categories: amusement, tenderness, calmness, anger, sadness, disgust, fear, and neutrality. Furthermore, we aimed to assess emotion-eliciting levels of short video clips, mostly taken by amateur video footage. In total, 104 film clips were tested online by rating several affective dimensions. Self-reported emotional experience was assessed in terms of intensity, discreteness, valence, and arousal. It was found that at least one of the existing film clips, most of the new film clips, and the short video clips were successful at eliciting medium to high levels of target emotions. However, we also observed overlaps between certain emotions (e.g., tenderness-sadness, anger-sadness-disgust, or fear-anxiety). The current results are mostly in line with previous databases, suggesting that film clips are efficient at eliciting a wide range of emotions where cultural background might play a role in the elicitation of certain emotions (e.g., amusement, anger, etc.). We hope that this extended emotion-eliciting film clips set (EGEFILM) will provide a rich resource for future emotion research both in Turkey and the international area.

Brain imaging studies of emotional well-being: a scoping review

This scoping review provides an overview of previous empirical studies that used brain imaging techniques to investigate the neural correlates of emotional well-being (EWB). We compiled evidence on this topic into one accessible and usable document as a foundation for future research into the relationship between EWB and the brain. PRISMA 2020 guidelines were followed. We located relevant articles by searching five electronic databases with 95 studies meeting our inclusion criteria. We explored EWB measures, brain imaging modalities, research designs, populations studied, and approaches that are currently in use to characterize and understand EWB across the literature. Of the key concepts related to EWB, the vast majority of studies investigated positive affect and life satisfaction, followed by sense of meaning, goal pursuit, and quality of life. The majority of studies used functional MRI, followed by EEG and event-related potential-based EEG to study the neural basis of EWB (predominantly experienced affect, affective perception, reward, and emotion regulation). It is notable that positive affect and life satisfaction have been studied significantly more often than the other three aspects of EWB (i.e., sense of meaning, goal pursuit, and quality of life). Our findings suggest that future studies should investigate EWB in more diverse samples, especially in children, individuals with clinical disorders, and individuals from various geographic locations. Future directions and theoretical implications are discussed, including the need for more longitudinal studies with ecologically valid measures that incorporate multi-level approaches allowing researchers to better investigate and evaluate the relationships among behavioral, environmental, and neural factors.

Systematic review registration

https://osf.io/t9cf6/.

Workload-dependent hemispheric asymmetries during the emotion-cognition interaction: a close-to-naturalistic fNIRS study

Introduction

We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study.

Methods

Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence.

Results

Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions.

Discussion

In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).

A Large Finer-grained Affective Computing EEG Dataset

Affective computing based on electroencephalogram (EEG) has gained increasing attention for its objectivity in measuring emotional states. While positive emotions play a crucial role in various real-world applications, such as human-computer interactions, the state-of-the-art EEG datasets have primarily focused on negative emotions, with less consideration given to positive emotions. Meanwhile, these datasets usually have a relatively small sample size, limiting exploration of the important issue of cross-subject affective computing. The proposed Finer-grained Affective Computing EEG Dataset (FACED) aimed to address these issues by recording 32-channel EEG signals from 123 subjects. During the experiment, subjects watched 28 emotion-elicitation video clips covering nine emotion categories (amusement, inspiration, joy, tenderness; anger, fear, disgust, sadness, and neutral emotion), providing a fine-grained and balanced categorization on both the positive and negative sides of emotion. The validation results show that emotion categories can be effectively recognized based on EEG signals at both the intra-subject and the cross-subject levels. The FACED dataset is expected to contribute to developing EEG-based affective computing algorithms for real-world applications.

Research on Emotion Recognition Method of Cerebral Blood Oxygen Signal Based on CNN-Transformer Network

In recent years, research on emotion recognition has become more and more popular, but there are few studies on emotion recognition based on cerebral blood oxygen signals. Since the electroencephalogram (EEG) is easily disturbed by eye movement and the portability is not high, this study uses a more comfortable and convenient functional near-infrared spectroscopy (fNIRS) system to record brain signals from participants while watching three different types of video clips. During the experiment, the changes in cerebral blood oxygen concentration in the 8 channels of the prefrontal cortex of the brain were collected and analyzed. We processed and divided the collected cerebral blood oxygen data, and used multiple classifiers to realize the identification of the three emotional states of joy, neutrality, and sadness. Since the classification accuracy of the convolutional neural network (CNN) in this research is not significantly superior to that of the XGBoost algorithm, this paper proposes a CNN-Transformer network based on the characteristics of time series data to improve the classification accuracy of ternary emotions. The network first uses convolution operations to extract channel features from multi-channel time series, then the features and the output information of the fully connected layer are input to the Transformer netork structure, and its multi-head attention mechanism is used to focus on different channel domain information, which has better spatiality. The experimental results show that the CNN-Transformer network can achieve 86.7% classification accuracy for ternary emotions, which is about 5% higher than the accuracy of CNN, and this provides some help for other research in the field of emotion recognition based on time series data such as fNIRS.

Cross-Subject Emotion Recognition Brain-Computer Interface Based on fNIRS and DBJNet

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.

The prefrontal cortex is differentially involved in implicit and explicit facial emotion processing: An fNIRS study

Despite extensive research, the differential roles of the prefrontal cortex (PFC) in implicit and explicit facial emotion processing remain elusive. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that can measure changes in both oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Currently, how HbO and HbR change during facial emotion processing remains unclear. Here, fNIRS was used to examine and compare PFC activation during implicit and explicit facial emotion processing. Forty young adults performed a facial-matching task that required either emotion discrimination (explicit task) or age discrimination (implicit task), and the activation of their PFCs was measured by fNIRS. Participants attempted the task on two occasions to determine whether their activation patterns were maintained over time. The PFC displayed increases in HbO and/or decreases in HbR during the implicit and explicit facial emotion tasks. Importantly, there were significantly greater changes in PFC HbO during the explicit task, whereas no significant difference in HbR changes between conditions was found. Between sessions, HbO changes were reduced across tasks, but the difference in HbO changes between the implicit and explicit tasks remained unchanged. The test-retest reliability of the behavioral measures was excellent, whereas that of fNIRS measures was mostly poor to fair. Thus, the PFC plays a specific role in recognizing facial expressions, and its differential involvement in implicit and explicit facial emotion processing can be consistently captured at the group level by changes in HbO. This study demonstrates the potential of fNIRS for elucidating the neural mechanisms underlying facial emotion recognition.

Predicting Moral Elevation Conveyed in Danmaku Comments Using EEGs

Moral elevation, the emotion that arises when individuals observe others' moral behaviors, plays an important role in determining moral behaviors in real life. While recent research has demonstrated the potential to decode basic emotions with brain signals, there has been limited exploration of affective computing for moral elevation, an emotion related to social cognition. To address this gap, we recorded electroencephalography (EEG) signals from 23 participants while they viewed videos that were expected to elicit moral elevation. More than 30,000 danmaku comments were extracted as a crowdsourcing tagging method to label moral elevation continuously at a 1-s temporal resolution. Then, by employing power spectra features and the least absolute shrinkage and selection operator regularized regression analyses, we achieved a promising prediction performance for moral elevation (prediction r = 0.44 ± 0.11). Our findings indicate that it is possible to decode moral elevation using EEG signals. Moreover, the small-sample neural data can predict the continuous moral elevation experience conveyed in danmaku comments from a large population.

The EEG microstate representation of discrete emotions

Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.

System Derived Spatial-Temporal CNN for High-Density fNIRS BCI

An intuitive and generalisable approach to spatial-temporal feature extraction for high-density (HD) functional Near-Infrared Spectroscopy (fNIRS) brain-computer interface (BCI) is proposed, demonstrated here using Frequency-Domain (FD) fNIRS for motor-task classification. Enabled by the HD probe design, layered topographical maps of Oxy/deOxy Haemoglobin changes are used to train a 3D convolutional neural network (CNN), enabling simultaneous extraction of spatial and temporal features. The proposed spatial-temporal CNN is shown to effectively exploit the spatial relationships in HD fNIRS measurements to improve the classification of the functional haemodynamic response, achieving an average F1 score of 0.69 across seven subjects in a mixed subjects training scheme, and improving subject-independent classification as compared to a standard temporal CNN.

The immediate effects of iTBS on the muscle activation pattern under challenging balance conditions in the patients with chronic low back pain: A preliminary study

Background

The patients with chronic low back pain (CLBP) showed impaired postural control, especially in challenging postural task. The dorsolateral prefrontal cortex (DLPFC) is reported to involve in the complex balance task, which required considerable attentional control. The effect of intermittent theta burst stimulation (iTBS) over the DLPFC to the capacity of postural control of CLBP patients is still unknown.

Methods

Participants diagnosed with CLBP received a single-session iTBS over the left DLPFC. All the participants completed the postural control tasks of single-leg (left/right) standing before and after iTBS. The activation changes of the DLPFC and M1 before and after iTBS were recorded by functional near-infrared spectroscopy (fNIRS). The activation pattern of the trunk [transversus abdominis (TrA), superficial lumbar multifidus (SLM)] and leg [tibialis anterior (TA), gastrocnemius medialis (GM)] muscles including root mean square (RMS) and co-contraction index (CCI) during single-leg standing were measured by surface electromyography (sEMG) before and after the intervention. The paired t-test was used to test the difference before and after iTBS. Pearson correlation analyses were performed to test the relationship between the oxyhemoglobin concentration and sEMG outcome variables (RMS and CCI).

Results

Overall, 20 participants were recruited. In the right-leg standing condition, compared with before iTBS, the CCI of the right TrA/SLM was significantly decreased (t = −2.172, p = 0.043), and the RMS of the right GM was significantly increased (t = 4.024, p = 0.001) after iTBS. The activation of the left DLPFC (t = 2.783, p = 0.012) and left M1 (t = 2.752, p = 0.013) were significantly decreased and the relationship between the left DLPFC and M1 was significant after iTBS (r = 0.575, p = 0.014). Correlation analysis showed the hemoglobin concentration of M1 was negatively correlated with the RMS of the right GM (r = −0.659, p = 0.03) and positively correlated between CCI of the right TrA/SLM (r = 0.503, p = 0.047) after iTBS. There was no significant difference in the brain or muscle activation change in the left leg-standing condition between before and after iTBS.

Conclusion

Intermittent theta burst stimulation over the left DLPFC seems to be able to improve the muscle activation pattern during postural control ability in challenging postural task, which would provide a new approach to the treatment of CLBP.

Pride: The Emotional Foundation of Social Rank Attainment

Pride is a self-conscious emotion, comprised of two distinct facets known as authentic and hubristic pride, and associated with a cross-culturally recognized nonverbal expression. Authentic pride involves feelings of accomplishment and confidence and promotes prosocial behaviors, whereas hubristic pride involves feelings of arrogance and conceit and promotes antisociality. Each facet of pride, we argue, contributes to a distinct means of attaining social rank: Authentic pride seems to promote prestige-a rank based on earned respect-whereas hubristic pride seems to promote dominance-a rank based on aggression and coercion. Both prestige and dominance are effective routes to power and influence in human groups, so both facets of pride are likely to be functional adaptations. Overall, the reviewed research suggests that pride is likely to be a human universal, critical for social relationships and rank attainment across human societies.

Low-dissipation optimization of the prefrontal cortex in the -12° head-down tilt position: A functional near-infrared spectroscopy study

INTRODUCTION

Our present study set out to investigate the instant state of the prefrontal cortex (PFC) in healthy subjects before and after placement in the -12°head-down tilt (HDT) position in order to explore the mechanism behind the low-dissipation optimization state of the PFC.

METHODS

40 young, right-handed healthy subjects (male: female = 20: 20) were enrolled in this study. Three resting state positions, 0°initial position, -12°HDT position, and 0°rest position were sequentially tested, each for 10 minutes. A continuous-wave functional near-infrared spectroscopy (fNIRS) instrument was used to assess the resting state hemodynamic data of the PFC. After preprocessing the hemodynamics data, we evaluated changes in resting-state functional connectivity (rsFC) level and beta values of PFC. The subjective visual analogue scale (VAS) was applied before and after the experiment. The presence of sleep changes or adverse reactions were also recorded.

RESULTS

Pairwise comparisons of the concentrations of oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and hemoglobin (HbT) revealed significant differences in the aforementioned positions. Specifically, the average rsFC of PFC showed a gradual increase throughout the whole process. In addition, based on graph theory, the topological properties of brain network, such as small-world network and nodal degree centrality were analyzed. The results show that global efficiency and small-world sigma (σ) value were differences between 0°initial and 0°rest.

DISCUSSION

In this study, placement in the -12°HDT had a significant effect on PFC function, mainly manifested as self-inhibition, decreased concentration of HbO in the PFC, and improved rsFC, which may provide ideas to the understanding and explanation of neurological diseases.

Effects of acute high-altitude exposure on working memory: A functional near-infrared spectroscopy study

INTRODUCTION

Inadequate oxygen availability may lead to impairment of neurocognitive functions. The aim of the present study was to investigate the effect of acute high-altitude exposure on the cerebral hemodynamic response and working memory.

METHODS

The same subjects performed working memory exercises with forward and backward digit span tasks both under normal oxygen conditions and in large simulated hypobaric hypoxia chambers, and a series of physiological parameters were evaluated. Functional near-infrared spectroscopy was used to measure cerebral blood flow changes in the dorsolateral prefrontal cortex (DLPFC) during the tasks.

RESULTS

Compared with normoxic conditions, under hypoxic conditions, the heart rate and blood pressure increased, blood oxygen saturation decreased significantly, and the forward task had similar accuracy and response time, while the backward task had lower accuracy and longer response time. Neuroimaging analysis showed increased activation in the DLPFC during the forward task and deactivation during the backward task under hypobaric hypoxia conditions.

CONCLUSION

Acute high-altitude exposure leads to physiological adaptations. The abnormal hemodynamic responses of the DLPFC to hypoxia at low pressure reveal the disruption of neurocognitive function by acute high-altitude exposure, which compromises complex cognitive functions, and provides a promising application for functional near infrared spectroscopy in the exploration of neural mechanisms in the brain during high-altitude exposure.

Comparing gratitude and pride: evidence from brain and behavior

Gratitude and pride are both positive emotions. Yet gratitude motivates people to help others and build up relationships, whereas pride motivates people to pursue achievements and build on self-esteem. Although these social outcomes are crucial for humans to be evolutionarily adaptive, no study so far has systematically compared gratitude and pride to understand why and how they can motivate humans differently. In this review, we compared gratitude and pride from their etymologies, cognitive prerequisites, motivational functions, and brain regions involved. By integrating the evidence from brain and behavior, we suggest that gratitude and pride share a common reward basis, yet gratitude is more related to theory of mind, while pride is more related to self-referential processing. Moreover, we proposed a cognitive neuroscientific model to explain the dynamics in gratitude and pride under a reinforcement learning framework.

Effects of different exercise intensities of race-walking on brain functional connectivity as assessed by functional near-infrared spectroscopy

Introduction

Race-walking is a sport that mimics normal walking and running. Previous studies on sports science mainly focused on the cardiovascular and musculoskeletal systems. However, there is still a lack of research on the central nervous system, especially the real-time changes in brain network characteristics during race-walking exercise. This study aimed to use a network perspective to investigate the effects of different exercise intensities on brain functional connectivity.

Materials and methods

A total of 16 right-handed healthy young athletes were recruited as participants in this study. The cerebral cortex concentration of oxyhemoglobin was measured by functional near-infrared spectroscopy in the bilateral prefrontal cortex (PFC), the motor cortex (MC) and occipital cortex (OC) during resting and race-walking states. Three specific periods as time windows corresponding to different exercise intensities were divided from the race-walking time of participants, including initial, intermediate and sprint stages. The brain activation and functional connectivity (FC) were calculated to describe the 0.01-0.1 Hz frequency-specific cortical activities.

Results

Compared to the resting state, FC changes mainly exist between MC and OC in the initial stage, while PFC was involved in FC changes in the intermediate stage, and FC changes in the sprint stage were widely present in PFC, MC and OC. In addition, from the initial-development to the sprint stage, the significant changes in FC were displayed in PFC and MC.

Conclusion

This brain functional connectivity-based study confirmed that hemodynamic changes at different exercise intensities reflected different brain network-specific characteristics. During race-walking exercise, more extensive brain activation might increase information processing speed. Increased exercise intensity could facilitate the integration of neural signals such as proprioception, motor control and motor planning, which may be an important factor for athletes to maintain sustained motor coordination and activity control at high intensity. This study was beneficial to understanding the neural mechanisms of brain networks under different exercise intensities.

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

Emotion Recognition: Photoplethysmography and Electrocardiography in Comparison

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.

Effects of Baduanjin imagery and exercise on cognitive function in the elderly: A functional near-infrared spectroscopy study

Objective

Cognitive function is essential in ensuring the quality of life of the elderly. This study aimed to investigate the effects of Baduanjin imagery and Baduanjin movement (a traditional Chinese health exercise, TCHE) on cognitive function in the elderly using functional near-infrared spectroscopy (fNIRS).

Methods

72 participants with a mean age of 66.92 years (SD = 4.77) were recruited for this study. The participants were randomly assigned to three groups: the Baduanjin imagery, the Baduanjin exercise, and the Control. Stroop task was used to record the accuracy and reaction times, and a near-infrared spectral brain imaging system was used to monitor the brain's oxy-hemoglobin concentration responses.

Results

(1) For the reaction times of Stroop incongruent tasks, the main effect of the test phase (F = 114.076, p < 0.001) and the interaction effect between test phase and group (F = 10.533, p < 0.001) were all significant. The simple effect analysis further demonstrated that the reaction times of the Baduanjin imagery group and Baduanjin exercise group in the post-test was faster than that in the pre-test (ps < 0.001); (2) Analysis of fNIRS data showed the significant interaction effect (F = 2.554, p = 0.013) between the test phase and group in the left dorsolateral prefrontal cortex. Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group (p = 0.005) and Baduanjin exercise group (p = 0.002) than in the control group; For the right inferior frontal gyrus, the interaction between the test phase and group was significant (F = 2.060, p = 0.044). Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group than in the control group (p = 0.001).

Conclusion

Baduanjin imagery and exercise positively affect cognitive performance; Baduanjin imagery and exercise activated the left dorsolateral prefrontal cortex; Baduanjin imagery activated the right inferior frontal gyrus, while Baduanjin exercise could not.

A pediatric near-infrared spectroscopy brain-computer interface based on the detection of emotional valence

Brain-computer interfaces (BCIs) are being investigated as an access pathway to communication for individuals with physical disabilities, as the technology obviates the need for voluntary motor control. However, to date, minimal research has investigated the use of BCIs for children. Traditional BCI communication paradigms may be suboptimal given that children with physical disabilities may face delays in cognitive development and acquisition of literacy skills. Instead, in this study we explored emotional state as an alternative access pathway to communication. We developed a pediatric BCI to identify positive and negative emotional states from changes in hemodynamic activity of the prefrontal cortex (PFC). To train and test the BCI, 10 neurotypical children aged 8–14 underwent a series of emotion-induction trials over four experimental sessions (one offline, three online) while their brain activity was measured with functional near-infrared spectroscopy (fNIRS). Visual neurofeedback was used to assist participants in regulating their emotional states and modulating their hemodynamic activity in response to the affective stimuli. Child-specific linear discriminant classifiers were trained on cumulatively available data from previous sessions and adaptively updated throughout each session. Average online valence classification exceeded chance across participants by the last two online sessions (with 7 and 8 of the 10 participants performing better than chance, respectively, in Sessions 3 and 4). There was a small significant positive correlation with online BCI performance and age, suggesting older participants were more successful at regulating their emotional state and/or brain activity. Variability was seen across participants in regards to BCI performance, hemodynamic response, and discriminatory features and channels. Retrospective offline analyses yielded accuracies comparable to those reported in adult affective BCI studies using fNIRS. Affective fNIRS-BCIs appear to be feasible for school-aged children, but to further gauge the practical potential of this type of BCI, replication with more training sessions, larger sample sizes, and end-users with disabilities is necessary.

fNIRS-based brain state transition features to signify functional degeneration after Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls.

APPROACH

In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls.

RESULTS

Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0:8200 and F score of 0:9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle.

SIGNIFICANCE

The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.

The influence of pride emotion on executive function: Evidence from ERP

BACKGROUND

The current study examined the influence of positive "basic" emotions on executive function; there is limited evidence about the influence of positive "self-conscious"emotions, such as pride, on executive functions processes.

METHODS

Pride is a status-related self-conscious emotion and the present research explored the influence of pride on the subcomponents of executive function, using three experiments that adopted the digit size-parity switching, N-back, and dual choice oddball paradigms.

RESULTS

The behavioral results suggested that cognitive load and behavior inhibition effects in the pride emotion were significantly higher than the neutral emotion. The ERP results showed that the pride emotion elicited smaller P3 difference wave for the switching task and dual choice oddball task. In the N-back task, the pride emotion elicited larger N1 amplitude and smaller P2 difference wave compared to the neutral emotion. A comparison among results from the three experiments indicated that pride emotion restrains all subcomponents of executive function, though with different manifestations of the impact.

CONCLUSION

Experiencing positive emotions is typically viewed as desirable and adaptive in educational settings; however, pride as a unique positive emotion may damage people's cognitive performance, indicating that we need to be cautious when performing cognitive operations in a pride mood.

Automatic Cognitive Fatigue Detection Using Wearable fNIRS and Machine Learning

Wearable sensors have increasingly been applied in healthcare to generate data and monitor patients unobtrusively. Their application for Brain-Computer Interfaces (BCI) allows for unobtrusively monitoring one's cognitive state over time. A particular state relevant in multiple domains is cognitive fatigue, which may impact performance and attention, among other capabilities. The monitoring of this state will be applied in real learning settings to detect and advise on effective break periods. In this study, two functional near-infrared spectroscopy (fNIRS) wearable devices were employed to build a BCI to automatically detect the state of cognitive fatigue using machine learning algorithms. An experimental procedure was developed to effectively induce cognitive fatigue that included a close-to-real digital lesson and two standard cognitive tasks: Corsi-Block task and a concentration task. Machine learning models were user-tuned to account for the individual dynamics of each participant, reaching classification accuracy scores of around 70.91 ± 13.67 %. We concluded that, although effective for some subjects, the methodology needs to be individually validated before being applied. Moreover, time on task was not a particularly determining factor for classification, i.e., to induce cognitive fatigue. Further research will include other physiological signals and human-computer interaction variables.

Similar brains blend emotion in similar ways: Neural representations of individual difference in emotion profiles

Our daily emotional experience is a complex construct that usually involves multiple emotions blended in a context-dependent manner. However, the co-occurring and context-dependent nature of human emotions was understated in previous studies when addressing the individual difference in emotional experiences. The present study proposed a situated and blended 'profile' perspective to characterize individualized emotional experiences. Eighty participants watched a series of emotional videos with their EEG recorded, and the individual differences in their emotion profiles were measured as the vector distances between their multidimensional emotion ratings for these video stimuli. This measure was found to be a reliable descriptor of individualized emotional experiences and could efficiently predict classical emotional complexity indices. More importantly, inter-subject representational analyses revealed that similar emotion profiles were associated with similar delta-band activities over the prefrontal and temporo-parietal regions and similar theta-band activities over the frontal regions. Furthermore, left- and right-lateralized temporo-parietal representations were observed for positive and negative emotion profiles, respectively. Our findings demonstrate the potential of taking a 'profile' perspective for understanding individual differences in human emotions.

The Immediate Effects of Intermittent Theta Burst Stimulation of the Cerebellar Vermis on Cerebral Cortical Excitability During a Balance Task in Healthy Individuals: A Pilot Study

Objective: This pilot study aimed to investigate the immediate effects of single-session intermittent theta-burst stimulation (iTBS) on the cerebellar vermis during a balance task, which could unveil the changes of cerebral cortical excitability in healthy individuals.

Subjects: A total of seven right-handed healthy subjects (26.86 ± 5.30 years) were included in this study.

Interventions: Each subject received single-session iTBS on cerebellar vermis in a sitting position.

Main Measures: Before and after the intervention, all subjects were asked to repeat the balance task of standing on the left leg three times. Each task consisted of 15 s of standing and 20 s of resting. Real-time changes in cerebral cortex oxygen concentrations were monitored with functional near-infrared spectroscopy (fNIRS). During the task, changes in blood oxygen concentration were recorded and converted into the mean HbO₂ for statistical analysis.

Results: After stimulation, the mean HbO₂ in the left SMA (P = 0.029) and right SMA (P = 0.043) significantly increased compared with baseline. However, no significant changes of mean HbO₂ were found in the bilateral dorsolateral prefrontal lobe (P > 0.05).

Conclusion: Single-session iTBS on the cerebellar vermis in healthy adults can increase the excitability of the cerebral cortex in the bilateral supplementary motor areas during balance tasks.

Clinical Trial Registration: [www.ClinicalTrials.gov], identifier [ChiCTR2100048915].

Quantified assessment of deep brain stimulation on Parkinson's patients with task fNIRS measurements and functional connectivity analysis: a pilot study

Background

Deep brain stimulation (DBS) has proved effective for Parkinson’s disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior.

Methods

Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (△HbO₂) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.

Results

We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.

Conclusions

Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.

Trial registration

Name: Accurate treatment under the multidisciplinary cooperative diagnosis and treatment model of Parkinson’s disease. Registration number is ChiCTR1900022715. Date of registration is April 23, 2019.

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees' safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm's performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Affective Neurofeedback Under Naturalistic Conditions: A Mini-Review of Current Achievements and Open Challenges

Affective neurofeedback training allows for the self-regulation of the putative circuits of emotion regulation. This approach has recently been studied as a possible additional treatment for psychiatric disorders, presenting positive effects in symptoms and behaviors. After neurofeedback training, a critical aspect is the transference of the learned self-regulation strategies to outside the laboratory and how to continue reinforcing these strategies in non-controlled environments. In this mini-review, we discuss the current achievements of affective neurofeedback under naturalistic setups. For this, we first provide a brief overview of the state-of-the-art for affective neurofeedback protocols. We then discuss virtual reality as a transitional step toward the final goal of "in-the-wild" protocols and current advances using mobile neurotechnology. Finally, we provide a discussion of open challenges for affective neurofeedback protocols in-the-wild, including topics such as convenience and reliability, environmental effects in attention and workload, among others.

Factor Structure of the "Top Ten" Positive Emotions of Barbara Fredrickson

In order to contribute to the consolidation in the field of Positive Psychology, we reinvestigated the factor structure of top 10 positive emotions of Barbara Fredrickson. Former research in experimental settings resulted in a three-cluster solution, which we tested with exploratory and confirmatory methodology against different factor models. Within our non-experimental data (N = 312), statistical evidence is presented, advocating for a single factor model of the 10 positive emotions. Different possible reasons for the deviating results are discussed, as well as the theoretical significance to various subfields in Positive Psychology (e.g., therapeutical interventions). Furthermore, the special role of awe within the study and its implications for further research in the field are discussed.

Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

Background

Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.

Objective

This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.

Method

A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.

Results

Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.

Conclusion

An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.

A systematic review of studies that used NIRS to measure neural activation during emotion processing in healthy individuals

Functional neuroimaging provides an avenue for earlier diagnosis and tailored treatment of psychological disorders characterised by emotional impairment. Near-infrared spectroscopy (NIRS) offers ecological advantages compared to other neuroimaging techniques and suitability of measuring regions involved in emotion functions. A systematic review was conducted to evaluate the capacity of NIRS to detect activation during emotion processing and to provide recommendations for future research. Following a comprehensive literature search, we reviewed 85 journal articles, which compared activation during emotional experience, regulation or perception with either a neutral condition or baseline period among healthy participants. The quantitative synthesis of outcomes was limited to thematical analysis, owing to the lack of standardisation between studies. Although most studies found increased prefrontal activity during emotional experience and regulation, the findings were more inconsistent for emotion perception. Some researchers reported increased activity during the task, some reported decreases, some no significant changes, and some reported mixed findings depending on the valence and region. We propose that variations in the cognitive task and stimuli, recruited sample, and measurement and analysis of data are the primary causes of inconsistency. Recommendations to improve consistency in future research by carefully considering the choice of population, cognitive task and analysis approach are provided.

Closed-loop neurostimulation for affective symptoms and disorders: An overview

Affective and anxiety disorders are the most prevalent and incident psychiatric disorders worldwide. Therapeutic approaches to these disorders using non-invasive brain stimulation (NIBS) and analogous techniques have been extensively investigated. In this paper, we discuss the combination of NIBS and neurofeedback in closed-loop setups and its application for affective symptoms and disorders. For this, we first provide a rationale for this combination by presenting some of the main original findings of NIBS, with a primary focus on transcranial magnetic stimulation (TMS), and neurofeedback, including protocols based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Then, we provide a scope review of studies combining real-time neurofeedback with NIBS protocols in the so-called closed-loop brain state-dependent neuromodulation (BSDS). Finally, we discuss the concomitant use of TMS and real-time functional near-infrared spectroscopy (fNIRS) as a possible solution to the current limitations of BSDS-based protocols for affective and anxiety disorders.

Subject-independent decoding of affective states using functional near-infrared spectroscopy

Affective decoding is the inference of human emotional states using brain signal measurements. This approach is crucial to develop new therapeutic approaches for psychiatric rehabilitation, such as affective neurofeedback protocols. To reduce the training duration and optimize the clinical outputs, an ideal clinical neurofeedback could be trained using data from an independent group of volunteers before being used by new patients. Here, we investigated if this subject-independent design of affective decoding can be achieved using functional near-infrared spectroscopy (fNIRS) signals from frontal and occipital areas. For this purpose, a linear discriminant analysis classifier was first trained in a dataset (49 participants, 24.65±3.23 years) and then tested in a completely independent one (20 participants, 24.00±3.92 years). Significant balanced accuracies between classes were found for positive vs. negative (64.50 ± 12.03%, p<0.01) and negative vs. neutral (68.25 ± 12.97%, p<0.01) affective states discrimination during a reactive block consisting in viewing affective-loaded images. For an active block, in which volunteers were instructed to recollect personal affective experiences, significant accuracy was found for positive vs. neutral affect classification (71.25 ± 18.02%, p<0.01). In this last case, only three fNIRS channels were enough to discriminate between neutral and positive affective states. Although more research is needed, for example focusing on better combinations of features and classifiers, our results highlight fNIRS as a possible technique for subject-independent affective decoding, reaching significant classification accuracies of emotional states using only a few but biologically relevant features.

Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between "like" vs. "dislike" out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

Effects of three different rehabilitation games' interaction on brain activation using functional near-infrared spectroscopy

OBJECTIVE

This study reveals the changes in brain activation due to different game interaction states based on functional near-infrared spectroscopy signals and discusses their significance for stroke rehabilitation.

APPROACH

The oxygenated hemoglobin concentration (Delta [HbO₂]) signals and the deoxygenated hemoglobin (Delta [HbR]) signals were recorded from the prefrontal cortex (PFC), the motor cortex (MC), the occipital lobe (OL) and the temporal lobe of 21 subjects (mean age: 24.6 ± 1.9 years old) in three game interaction states: physical, motion-sensing, and button-push training. The subjects were also asked to complete user-satisfaction survey scales after the experiment.

MAIN RESULTS

Compared with the button-training state, several channels in the PFC and MC region of the physical-training state were significantly altered as were several channels in the RMC region of the motion-sensing training state (P < 0.05 after adjustment). The motion-sensing state of the PFC had a significant correlation with that of the MC and the OL. The subjective scale results show that the acceptability of the physical and motion-sensing states was greater than the acceptability of the button-push training state.

SIGNIFICANCE

The results show that the brain regions responded more strongly when activated by the physical and motion-sensing states compared with the button-push training state, and the physical and motion-sensing states are more conducive to the rehabilitation of the nervous system. The design of rehabilitation products for stroke patients is discussed and valuable insights are offered to support the selection of better interactive training methods.

A Review on Research and Evaluation Methods for Investigating Self-Transcendence

Self-transcendence has been characterized as a decrease in self-saliency (ego disillusionment) and increased connection, and has been growing in research interest in the past decade. Several measures have been developed and published with some degree of psychometric validity and reliability. However, to date, there has been no review systematically describing, contrasting, and evaluating the different methodological approaches toward measuring self-transcendence including questionnaires, neurological and physiological measures, and qualitative methods. To address this gap, we conducted a review to describe existing methods of measuring self-transcendence, evaluate the strengths and weaknesses of these methods, and discuss research avenues to advance assessment of self-transcendence, including recommendations for suitability of methods given research contexts.