EPOS: EEG Processing Open-Source Scripts

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Electrophysiological correlates of why humans deviate from rational decision-making: A registered replication study

In contrast to rational choice theory predicting humans to optimize expected utilities of choices, humans deviate from rational behavior in decision-making paradigms. Hewig et al. (2011) explored affective correlates of decision-making in the ultimatum game (UG) and the dictator game (DG). They found that feedback-related negativity (FRN), subjective valence ratings, and autonomic nervous system activity predicted rejection of monetary offers. This registered replication aimed to validate and extend these findings. Although behavioral patterns and results of subjective ratings closely matched the original study, not all psychophysiological effects were successfully replicated. Firstly, we could not replicate the reported effects of autonomic nervous system activity. Secondly, a quadratic instead of the originally proposed linear relation between the offer and the FRN emerged, possibly driven by the offer evaluation in economic games and the rewarding anticipation of successful punishment for low offers. Thirdly, P3 amplitudes mirrored the quadratic offer response pattern, generally peaking for the lowest offer. In contrast to the original study, P3 responses were larger in the UG compared with the DG. Finally, our findings indicate that participant-related higher midfrontal theta activation predicted lower acceptance behavior in the UG, with a systematic dampening effect for fairer offers. This highlights cognitive control as a crucial mechanism in economic decision-making to overcome behavioral defaults. Overall, our results conceptually support the original conclusion that decision-making in economic games is non-rational and dependent on the objective situation as well as emotional and neural markers, though not precisely as suggested by Hewig et al. (2011).

Machine learning of brain-specific biomarkers from EEG

BACKGROUND

Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS).

METHODS

We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses.

FINDINGS

Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals.

INTERPRETATION

We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML.

FUNDING

All authors have been working for F. Hoffmann-La Roche Ltd.

The association of dispositional anxiety with the NoGo N2 under relaxation instruction vs. speed/accuracy instruction

Prior research suggests that cognitive control, indicated by NoGo N2 amplitudes in Go/NoGo tasks, is associated with dispositional anxiety. This negative association tends to be reduced in anxiety-enhancing experimental conditions. However, anxiety-reducing conditions have not yet been investigated systematically. Thus, the present study compares the effect of a relaxation instruction with the conventional speed/accuracy instruction in a Go/NoGo task on the correlation of the NoGo N2 with two subconstructs of dispositional anxiety, namely anxious apprehension and anxious arousal. As the test of differences between correlations needs considerable statistical power, the present study was included into the multi-lab CoScience Project. The hypotheses, manipulation checks, and the main path of pre-processing and statistical analysis were preregistered. Complete data sets of 777 participants were available for data analysis. Preregistered general linear models revealed that the different instructions of the task (speed/accuracy vs. relaxation) had no effect on the association between dispositional anxiety and the NoGo N2 amplitude in general. This result was supported by Cooperative-Forking-Path analysis. In contrast, a preregistered latent growth model with categorical variables revealed that anxious arousal was a negative predictor of the NoGo N2 intercept and a positive predictor of the NoGo N2 slope. Non-preregistered growth models, allowing for correlations of anxious apprehension with anxious arousal, revealed that higher anxious apprehension scores were associated with more negative NoGo N2 amplitudes with increased relaxation. Results are discussed in the context of the compensatory error monitoring hypothesis and the revised Reinforcement Sensitivity Theory.

Exploration of the influence of the quantification method and reference scheme on feedback-related negativity and standardized measurement error of feedback-related negativity amplitudes in a trust game

Various approaches have been taken over the years to quantify event-related potential (ERP) responses and these approaches may vary in their utility connecting empirical research and scientific claims. In this work we compared different quantification methods as well as the influence of three reference methods (linked mastoids, average reference, and current source density) on the resulting ERP amplitude. We use the experimental effects and effect sizes (Cohen's d) to evaluate the different methodological variants and we calculate intraclass correlation coefficients (ICC). In addition, the bootstrapped standard error of the means (SME, Luck et al., 2021), which was recently suggested as a quality criterion for ERP research, is used for this purpose. Our example for an ERP is the feedback-related negativity (FRN) to feedback about trustee behavior in a trust game with participants in the trustor position. We found that the quantification methods concerning the FRN influenced the absolute value of condition effects in the experimental paradigm. Yet, the patterns of effects were detected by all chosen methods, except for the 'individual difference wave'-based peak window approach. In addition, our findings stress the importance of checking the reference electrodes concerning effects of the experimental conditions. Furthermore, interactions of topographical distribution and reference choice should be considered. Finally, we were able to show that the SME is lower for more datapoints that are given in the quantification period of the FRN, and higher for more negative FRN amplitudes. These biases may lead to divergence of SME and effect size detection. Therefore, if the SME was used to compare different processing choices one should consider controlling for these important aspects of the data and possibly include other quality criteria like effect sizes.

Study on the Effect of Dalbergia pinnata (Lour.) Prain Essential Oil on Electroencephalography upon Stimulation with Different Auditory Effects

Dalbergia pinnata (Lour.) Prain (D. pinnata) is a valuable medicinal plant, and its volatile parts have a pleasant aroma. In recent years, there have been a large number of studies investigating the effect of aroma on human performance. However, the effect of the aroma of D. pinnata on human psychophysiological activity has not been reported. Few reports have been made about the effects of aroma and sound on human electroencephalographic (EEG) activity. This study aimed to investigate the effects of D. pinnata essential oil in EEG activity response to various auditory stimuli. In the EEG study, 30 healthy volunteers (15 men and 15 women) participated. The electroencephalogram changes of participants during the essential oil (EO) of D. pinnata inhalation under white noise, pink noise and traffic noise stimulations were recorded. EEG data from 30 electrodes placed on the scalp were analyzed according to the international 10-20 system. The EO of D. pinnata had various effects on the brain when subjected to different auditory stimuli. In EEG studies, delta waves increased by 20% in noiseless and white noise environments, a change that may aid sleep and relaxation. In the presence of pink noise and traffic noise, alpha and delta wave activity (frontal pole and frontal lobe) increased markedly when inhaling the EO of D. pinnata, a change that may help reduce anxiety. When inhaling the EO of D. pinnata with different auditory stimuli, women are more likely to relax and get sleepy compared to men.

An explorative multiverse study for extracting differences in P3 latencies between young and old adults

It is well established that P3 latencies increase with age. Investigating these age-related differences requires numerous methodological decisions, resulting in pipelines of great variation. The aim of the present work was to investigate the effects of different analytical pipelines on the age-related differences in P3 latencies in real data. Therefore, we conducted an explorative multiverse study and varied the low-pass filter (4 Hz, 8 Hz, 16 Hz, 32 Hz, and no filter), the latency type (area vs. peak), the level of event-related potential analysis (single participant vs. jackknifing), and the extraction method (manual vs. automated). Thirty young (18-21 years) and 30 old (50-60 years) participants completed three tasks (Nback task, Switching task, Flanker task), while an EEG was recorded. The results show that different analysis strategies can have a tremendous impact on the detection and magnitude of the age effect, with effect sizes ranging from 0% to 88% explained variance. Likewise, regarding the psychometric properties of P3 latencies, we found that the reliabilities fluctuated between rtt  = .20 and 1.00, while the homogeneities ranged from rh  = -.12 to .90. Based on predefined criteria, we found that the most effective pipelines relied on a manual extraction based on a single participant's data. For peak latencies, manual extraction performed well for all filters except for 4 Hz, while for area latencies, filters above 8 Hz produced desirable results. Furthermore, our findings add to the evidence that jackknifing combined with peak latencies can lead to inconclusive results.

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.

Inhibition of midfrontal theta with transcranial ultrasound explains greater approach versus withdrawal behavior in humans

Recent reviews highlighted low-intensity transcranial focused ultrasound (TUS) as a promising new tool for non-invasive neuromodulation in basic and applied sciences. Our preregistered double-blind within-subjects study (N = 152) utilized TUS targeting the right prefrontal cortex, which, in earlier work, was found to positively enhance self-reported global mood, decrease negative states of self-reported emotional conflict (anxiety/worrying), and modulate related midfrontal functional magnetic resonance imaging activity in affect regulation brain networks. To further explore TUS effects on objective physiological and behavioral variables, we used a virtual T-maze task that has been established in prior studies to measure motivational conflicts regarding whether participants execute approach versus withdrawal behavior (with free-choice responses via continuous joystick movements) while allowing to record related electroencephalographic data such as midfrontal theta activity (MFT). MFT, a reliable marker of conflict representation on a neuronal level, was of particular interest to us since it has repeatedly been shown to explain related behavior, with relatively low MFT typically preceding approach-like risky behavior and relatively high MFT typically preceding withdrawal-like risk aversion. Our central hypothesis is that TUS decreases MFT in T-maze conflict situations and thereby increases approach and reduces withdrawal. Results indicate that TUS led to significant MFT decreases, which significantly explained increases in approach behavior and decreases in withdrawal behavior. This study expands TUS evidence on a physiological and behavioral level with a large sample size of human subjects, suggesting the promise of further research based on this distinct TUS-MFT-behavior link to influence conflict monitoring and its behavioral consequences. Ultimately, this can serve as a foundation for future clinical work to establish TUS interventions for emotional and motivational mental health.

Investigating the role of the right inferior frontal gyrus in control perception: A double-blind cross-over study using ultrasonic neuromodulation

Being able to control inner and environmental states is a basic need of living creatures. The perception of such control is based on the perceived ratio of outcome probabilities given the presence and the absence of agentic behavior. If an organism believes that options exist to change the probability of a given outcome, control perception (CP) may emerge. Nonetheless, regarding this model, not much is known about how the brain processes CP from this information. This study uses low-intensity transcranial focused ultrasound neuromodulation in a randomized-controlled double blind cross-over design to investigate the impact of the right inferior frontal gyrus of the lateral prefrontal cortex (lPFC) on this process. 39 healthy participants visited the laboratory twice (once in a sham, once in a neuromodulation condition) and rated their control perception regarding a classical control illusion task. EEG alpha and theta power density were analyzed in a hierarchical single trial-based mixed modeling approach. Results indicate that the litFUS neuromodulation changed the processing of stimulus probability without changing CP. Furthermore, neuromodulation of the right lPFC was found to modulate mid-frontal theta by altering its relationship with self-reported effort and worrying. While these data indicate lateral prefrontal sensitivity to stimulus probability, no evidence emerged for the dependency of CP on this processing.

EEG-based functional connectivity for tactile roughness discrimination

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

Transcranial focused ultrasound modulates the emergence of learned helplessness via midline theta modification

BACKGROUND

Helplessness and hopelessness are states closely related to depressive disorders. They ensue following the anticipated absence of valid behavioral options to alleviate an aversive state. One neural structure involved in the detection of such options, is the cingulate cortex (CC), which conveys the evaluation of behavior against the value of its outcome. Accordingly, CC-related EEG measures are thought to correlate to feedback evaluation but also to anxious and conflict-related states, signaling the need for further adaptation to current requirements. Against this background, this study investigated the role of CC functioning in the emergence and prevention of helpless/hopeless states by applying low-intensity transcranial focused ultrasound (litFUS) neuromodulation prior to a learned helplessness task.

METHOD

In a randomized controlled double blind experimental setup with 55 participants, litFUS was used to inhibit the right lateral prefrontal cortex (lPFC), a region closely connected to CC modulation. Participants were instructed to play 8 games of chess against a computer that was unbeatable to them, while an EEG was recorded. Theta oscillation in midline electrodes, playing performance and self-rate measures on cognitive, affective and physiological parameters were assessed.

RESULTS

The results show a considerable influence of litFUS neuromodulation of the lPFC on midline theta activity (Fz and Pz electrode position) which in turn correlated to several psychological variables including self-report data on emotion, cognition, and arousal as well as behavioral measures (playing performance).

LIMITATIONS

Due to the small sample size and sample characteristics, results cannot be generalized to the field of clinical application. A replication of results in larger samples and in context of other sonication parameters is needed to increase the robustness of results and to maximize the efficacy of litFUS application.

CONCLUSIONS

We conclude that the development of learned helplessness/hopelessness could be positively influenced in its course by litFUS neuromodulation of the right lPFC. In line with previous results, especially the posterior midline electrode Pz seems to be an interesting target for further research in this field as theta activity at this electrode is correlated to control perception and motivated behavior. To our knowledge, this is the first study to use neuromodulation to monitor and manipulate the development of helplessness in the laboratory.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

Altered neural correlates of optimal decision-making in individuals with depressive status

Making optimal decisions by computing risk and benefit is necessary for humans. However, whether individuals with depressive status could utilize the optimal strategy to guide decision and its neural correlates remain unclear. The current study explored these issues by combining a decision task and high temporal-resolution electroencephalogram (EEG). The decision task involved an eight-box trial in which participants successively decided whether to open a box containing a potential reward or punishment, deciding to stop guaranteed they would retain the rewards already accumulated. Theoretically, the optimal strategy in the task was to stop at the fourth box, which had the largest expected value. We found that individuals with depressive status stopped fewer trials at the fourth box, relative to healthy controls, indicating their impaired optimal strategy during decision-making. Moreover, compared to healthy controls, individuals with depressive status showed weaker P2 amplitude and weaker beta-band oscillation at the frontocentral scalp when deciding whether to open the fourth box. Additionally, for healthy controls but not for individuals with depressive status, the P2 amplitude fully mediated the relationship between participants' degree of expected benefit (as reflected by the recreational risk-taking scale) and the frequency of trials stopped at the fourth box. Overall, this study revealed that the P2 amplitude and beta-band oscillation might explain the altered optimal decision-making in individuals with depressive status.

Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.

Study on the Effect of Mentha × piperita L. Essential Oil on Electroencephalography upon Stimulation with Different Visual Effects

Essential oils have long been used to fight infections and treat various diseases. Peppermint (Mentha × piperita L.) is an herbal medicine that has been widely used in daily life since ancient times, and it has a wide range of applications in food, cosmetics, and medicine. Mint oil is refreshing because of its cool and comfortable smell; therefore, it is often used in ethnopharmacological studies. The present study investigated the effects of peppermint essential oil in electroencephalographic activity response to various visual stimuli. The electroencephalographic changes of participants during peppermint essential oil inhalation under white, red, and blue colour stimulations were recorded. A rapid Fourier transform analysis was used to examine the electroencephalograph power spectra of the various microstates induced by inhaling the oils. Peppermint essential oil had various effects on the brain when subjected to different visual stimuli. Alpha waves increased in the prefrontal area in the white-sniffing group, which facilitated learning and thinking. In the blue-sniffing group, the changes were less pronounced than those in the red group, and the increased alpha wave activity in the occipital area was more controlled, indicating that the participants’ visual function increased in this state. Based on EEG investigations, this is the first study to indicate that vision influences the effects of peppermint essential oils. Hence, the results of this study support the use of essential oils in a broader context to serve as a resource for future studies on the effects of different types of essential oils.

Mental chronometry in big noisy data

Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure.

Scorepochs: A Computer-Aided Scoring Tool for Resting-State M/EEG Epochs

M/EEG resting-state analysis often requires the definition of the epoch length and the criteria in order to select which epochs to include in the subsequent steps. However, the effects of epoch selection remain scarcely investigated and the procedure used to (visually) inspect, label, and remove bad epochs is often not documented, thereby hindering the reproducibility of the reported results. In this study, we present Scorepochs, a simple and freely available tool for the automatic scoring of resting-state M/EEG epochs that aims to provide an objective method to aid M/EEG experts during the epoch selection procedure. We tested our approach on a freely available EEG dataset containing recordings from 109 subjects using the BCI2000 64 channel system.

Timing of adoption is associated with electrophysiological brain activity and externalizing problems among children adopted internationally

This study investigated middle childhood resting electroencephalography (EEG) and behavioral adjustment in 35 internationally adopted children removed from early caregiving adversity between 6 and 29 months of age. Older age of adoption was associated with more immature or atypical profiles of middle childhood cortical function, based on higher relative theta power (4-6 Hz), lower relative alpha power (7-12 Hz), lower peak alpha frequency, and lower absolute beta (13-20 Hz) and gamma (21-50 Hz) power. More immature or atypical EEG spectral power indirectly linked older age of adoption with increased risk for externalizing problems in middle childhood. The findings add to existing evidence linking duration of early adverse exposures with lasting effects on brain function and behavioral regulation even years after living in a stable adoptive family setting. Findings underscore the need to minimize and prevent children's exposures to early caregiving adversity, especially in the first years of life. They call for innovative interventions to support neurotypical development in internationally adopted children at elevated risk.

Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline

Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (M_age  = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.

On second thought … the influence of a second stage in the ultimatum game on decision behavior, electro-cortical correlates and their trait interrelation

Previous EEG research only investigated one stage ultimatum games (UGs). We investigated the influence of a second bargaining stage in an UG concerning behavioral responses, electro-cortical correlates and their moderations by the traits altruism, anger, anxiety, and greed in 92 participants. We found that an additional stage led to more rejection in the 2-stage UG (2SUG) and that increasing offers in the second stage compared to the first stage led to more acceptance. The FRN during a trial was linked to expectance evaluation concerning the fairness of the offers, while midfrontal theta was a marker for the needed cognitive control to overcome the respective default behavioral pattern. The FRN responses to unfair offers were more negative for either low or high altruism in the UG, while high trait anxiety led to more negative FRN responses in the first stage of 2SUG, indicating higher sensitivity to unfairness. Accordingly, the mean FRN response, representing the trait-like general electrocortical reactivity to unfairness, predicted rejection in the first stage of 2SUG. Additionally, we found that high trait anger led to more rejections for unfair offer in 2SUG in general, while trait altruism led to more rejection of unimproving unfair offers in the second stage of 2SUG. In contrast, trait anxiety led to more acceptance in the second stage of 2SUG, while trait greed even led to more acceptance if the offer was worse than in the stage before. These findings suggest, that 2SUG creates a trait activation situation compared to the UG.

EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction

Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.

Neural Correlates of Successful Costly Punishment in the Ultimatum Game on a Trial-by-Trial Basis

Costly punishment describes decisions of an interaction partner to punish an opponent for violating rules of fairness at the expense of personal costs. Here, we extend the interaction process by investigating the impact of a socio-emotional reaction of the opponent in response to the punishment that indicates whether punishment was successful or not. In a modified Ultimatum game, emotional facial expressions of the proposer in response to the decision of the responder served as feedback stimuli. We found that both honored reward following acceptance of an offer (smiling compared to neutral facial expression) and successful punishment (sad compared to neutral facial expression) elicited a reward positivity, indicating that punishment was the intended outcome. By comparing the pattern of results with a probabilistic learning task, we show that the reward positivity on sad facial expressions was specific for the context of costly punishment. Additionally, acceptance rates on a trial-by-trial basis were altered according to P3 amplitudes in response to the emotional facial reaction of the proposer. Our results are in line with the concept of costly punishment as an intentional act following norm-violating behavior. Socio-emotional stimuli have an important influence on the perception and behavior in economic bargaining.

The Influence of Mental Imagery Expertise of Pen and Paper Players versus Computer Gamers upon Performance and Electrocortical Correlates in a Difficult Mental Rotation Task

We investigated the influence of mental imagery expertise in 15 pen and paper role-players as an expert group compared to the gender-matched control group of computer role-players in the difficult Vandenberg and Kuse mental rotation task. In this task, the participants have to decide which two of four rotated figures match the target figure. The dependent measures were performance speed and accuracy. In our exploratory investigation, we further examined midline frontal theta band activation, parietal alpha band activation, and parietal alpha band asymmetry in EEG as indicator for the chosen rotation strategy. Additionally, we explored the gender influence on performance and EEG activation, although a very small female sample section was given. The expected gender difference concerning performance accuracy was negated by expertise in pen and paper role-playing women, while the gender-specific difference in performance speed was preserved. Moreover, gender differences concerning electro-cortical measures revealed differences in rotation strategy, with women using top-down strategies compared to men, who were using top-down strategies and active inhibition of associative cortical areas. These strategy uses were further moderated by expertise, with higher expertise leading to more pronounced activation patters, especially during successful performance. However, due to the very limited sample size, the findings of this explorative study have to be interpreted cautiously.