Exceed Chance Levels Research Articles

Limited value of EEG source imaging for decoding hand movement and imagery in youth with brain lesions

ABSTRACT Brain–computer interfaces based on electroencephalography (EEG) often exhibit unreliable performance in the classification of motor tasks. Recent research has shown that EEG source imaging (ESI) has the potential to outperform sensor domain approaches in various movement decoding tasks. However, ESI research to date has predominantly focused on the adult population, so its performance in youth with disabilities is unknown. In this study, we compared the offline classification performance of two ESI approaches (with and without modeling white matter conductivity anisotropy) to that of a sensor domain approach in the classification of left- versus right-hand movement execution and imagery tasks. Magnetic resonance images (MRI) were acquired from nine pediatric participants with brain lesions. Subsequently, cortical activity was recorded from 64 channels. MRI data were used to estimate participant-specific EEG sources. Various feature extraction and classification approaches were investigated in both sensor and source domains. Generally, ESI classification performance did not exceed chance levels and was statistically equivalent to sensor approaches except for isolated participants. However, ESI offered +9.61% improvement over the sensor domain (p = 0.031) in decoding motor execution in a participant with unilateral ventriculomegaly. Future research ought to delineate the specific task and participant characteristics which warrant the source domain approach.

The slow emergence of gaze- and point-following: A longitudinal study of infants from 4 to 12 months.

Acquisition of visual attention-following skills, notably gaze- and point-following, contributes to infants' ability to share attention with caregivers, which in turn contributes to social learning and communication. However, the development of gaze- and point-following in the first 18 months remains controversial, in part because of different testing protocols and standards. To address this, we longitudinally tested N=43 low-risk, North American middle-class infants' tendency to follow gaze direction, pointing gestures, and gaze-and-point combinations. Infants were tested monthly from 4 to 12 months of age. To control motivational differences, infants were taught to expect contingent reward videos in the target locations. No-cue trials were included to estimate spontaneous target fixation rates. A comparison sample (N=23) was tested at 9 and 12 months to estimate practice effects. Results showed gradual increases in both gaze- and point-following starting around 7 months, and modest month-to-month individual stability from 8 to 12 months. However, attention-following did not exceed chance levels until after 6 months. Infants rarely followed cues to locations behind them, even at 12 months. Infants followed combined gaze-and-point cues more than gaze alone, and followed points at intermediate levels (not reliably different from the other cues). The comparison group's results showed that practice effects did not explain the age-related increase in attention-following. The results corroborate and extend previous findings that North American middle-class infants' attention-following in controlled laboratory settings increases slowly and incrementally between 6 and 12 months of age. RESEARCH HIGHLIGHTS: A longitudinal experimental study documented the emergence and developmental trajectories of North American middle-class infants' visual attention-following skills, including gaze-following, point-following, and gaze-and-point-following. A new paradigm controlled for factors including motivation, attentiveness, and visual-search baserates. Motor development was ruled out as a predictor or limiter of the emergence of attention-following. Infants did not follow attention reliably until after 6 months, and following increased slowly from 7 to 12 months. Infants' individual trajectories showed modest month-to-month stability from 8 to 12 months of age.

Complex visual discrimination is impaired after right, but not left, anterior temporal lobectomy.

The prevailing view in human cognitive neuroscience associates the medial temporal lobes (MTLs) with declarative memory. Compelling experimental evidence has, however, demonstrated that these regions are specialized according to the representations processed, irrespective of the cognitive domain assessed. This account was supported by the study of patients with bilateral medial temporal amnesia, who exhibit impairments in perceptual tasks involving complex visual stimuli. Yet, little is known regarding the impact of unilateral MTL damage on complex visual abilities. To address this issue, we administered a visual matching task to 20 patients who underwent left (N = 12) or right (N = 8) anterior temporal lobectomy for drug-resistant epilepsy and to 38 healthy controls. Presentation viewpoint was manipulated to increase feature ambiguity, as this is critical to reveal impairments in perceptual tasks. Similar to control participants, patients with left-sided damage succeeded in all task conditions. In contrast, patients with right-sided damage had decreased accuracy compared with that of the other two groups, as well as increased response time. Notably, the accuracy of those with right-sided damage did not exceed chance level when feature ambiguity was high (i.e., when stimuli were presented from different viewpoints) for the most complex classes of stimuli (i.e., scenes and buildings, compared with single objects). The pattern reported in bilateral patients in previous studies was therefore reproduced in patients with right, but not left, resection. These results suggest that the complex visual-representation functions supported by the MTL are right-lateralized, and raise the question as to how the representational account of these regions applies to representations supported by left MTL regions.

Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19

Despite the high contagion and mortality rates that have accompanied the coronavirus disease-19 (COVID-19) pandemic, the clinical presentation of the syndrome varies greatly from one individual to another. Potential host factors that accompany greater risk from COVID-19 have been sought and schizophrenia (SCZ) patients seem to present more severe COVID-19 than control counterparts, with certain gene expression similarities between psychiatric and COVID-19 patients reported. We used summary statistics from the last SCZ, bipolar disorder (BD), and depression (DEP) meta-analyses available on the Psychiatric Genomics Consortium webpage to calculate polygenic risk scores (PRSs) for a target sample of 11,977 COVID-19 cases and 5943 subjects with unknown COVID-19 status. Linkage disequilibrium score (LDSC) regression analysis was performed when positive associations were obtained from the PRS analysis. The SCZ PRS was a significant predictor in the case/control, symptomatic/asymptomatic, and hospitalization/no hospitalization analyses in the total and female samples; and of symptomatic/asymptomatic status in men. No significant associations were found for the BD or DEP PRS or in the LDSC regression analysis. SNP-based genetic risk for SCZ, but not for BD or DEP, may be associated with higher risk of SARS-CoV-2 infection and COVID-19 severity, especially among women; however, predictive accuracy barely exceeded chance level. We believe that the inclusion of sexual loci and rare variations in the analysis of genomic overlap between SCZ and COVID-19 will help to elucidate the genetic commonalities between these conditions.

Functional Connectivity of the Nucleus Accumbens and Changes in Appetite in Patients With Depression

Major depressive disorder (MDD) is characterized by a substantial burden on health, including changes in appetite and body weight. Heterogeneity of depressive symptoms has hampered the identification of biomarkers that robustly generalize to most patients, thus calling for symptom-based mapping. To define the functional architecture of the reward circuit subserving increases vs decreases in appetite and body weight in patients with MDD by specifying their contributions and influence on disease biomarkers using resting-state functional connectivity (FC). In this case-control study, functional magnetic resonance imaging (fMRI) data were taken from the Marburg-Münster FOR 2107 Affective Disorder Cohort Study (MACS), collected between September 2014 and November 2016. Cross-sectional data of patients with MDD (n = 407) and healthy control participants (n = 400) were analyzed from March 2018 to June 2022. Changes in appetite during the depressive episode and their association with FC were examined using fMRI. By taking the nucleus accumbens (NAcc) as seed of the reward circuit, associations with opposing changes in appetite were mapped, and a sparse symptom-specific elastic-net model was built with 10-fold cross-validation. Among 407 patients with MDD, 249 (61.2%) were women, and the mean (SD) age was 36.79 (13.4) years. Reduced NAcc-based FC to the ventromedial prefrontal cortex (vmPFC) and the hippocampus was associated with reduced appetite (vmPFC: bootstrap r = 0.13; 95% CI, 0.02-0.23; hippocampus: bootstrap r = 0.15; 95% CI, 0.05-0.26). In contrast, reduced NAcc-based FC to the insular ingestive cortex was associated with increased appetite (bootstrap r = -0.14; 95% CI, -0.24 to -0.04). Critically, the cross-validated elastic-net model reflected changes in appetite based on NAcc FC and explained variance increased with increasing symptom severity (all patients: bootstrap r = 0.24; 95% CI, 0.16-0.31; patients with Beck Depression Inventory score of 28 or greater: bootstrap r = 0.42; 95% CI, 0.25-0.58). In contrast, NAcc FC did not classify diagnosis (MDD vs healthy control). In this study, NAcc-based FC reflected important individual differences in appetite and body weight in patients with depression that can be leveraged for personalized prediction. However, classification of diagnosis using NAcc-based FC did not exceed chance levels. Such symptom-specific associations emphasize the need to map biomarkers onto more confined facets of psychopathology to improve the classification and treatment of MDD.

Investigation of Brain Activation Patterns Related to the Feminization or Masculinization of Body and Face Images across Genders.

Previous studies demonstrated sex-related differences in several areas of the human brain, including patterns of brain activation in males and females when observing their own bodies and faces (versus other bodies/faces or morphed versions of themselves), but a complex paradigm touching multiple aspects of embodied self-identity is still lacking. We enrolled 24 healthy individuals (12 M, 12 F) in 3 different fMRI experiments: the vision of prototypical body silhouettes, the vision of static images of the face of the participants morphed with prototypical male and female faces, the vision of short videos showing the dynamic transformation of the morphing. We found differential sexual activations in areas linked to self-identity and to the ability to attribute mental states: In Experiment 1, the male group activated more the bilateral thalamus when looking at sex congruent body images, while the female group activated more the middle and inferior temporal gyrus. In Experiment 2, the male group activated more the supplementary motor area when looking at their faces; the female group activated more the dorsomedial prefrontal cortex (dmPFC). In Experiment 3, the female group activated more the dmPFC when observing either the feminization or the masculinization of their face. The defeminization produced more activations in females in the left superior parietal lobule and middle occipital gyrus. The performance of all classifiers built using single ROIs exceeded chance level, reaching an area under the ROC curves > 0.85 in some cases (notably, for Experiment 2 using the V1 ROI). The results of the fMRI tasks showed good agreement with previously published studies, even if our sample size was small. Therefore, our functional MRI protocol showed significantly different patterns of activation in males and females, but further research is needed both to investigate the gender-related differences in activation when observing a morphing of their face/body, and to validate our paradigm using a larger sample.

Anticipating the direction of symptom progression using critical slowing down: a proof-of-concept study

BackgroundAs complex dynamic systems approach a transition, their dynamics change. This process, called critical slowing down (CSD), may precede transitions in psychopathology as well. This study investigated whether CSD may also indicate the direction of future symptom transitions, i.e., whether they involve an increase or decrease in symptoms.MethodsIn study 1, a patient with a history of major depression monitored their mental states ten times a day for almost eight months. Study 2 used data from the TRAILS TRANS-ID study, where 122 young adults at increased risk of psychopathology (mean age 23.64±0.67 years, 56.6% males) monitored their mental states daily for six consecutive months. Symptom transitions were inferred from semi-structured diagnostic interviews. In both studies, CSD direction was estimated using moving-window principal component analyses.ResultsIn study 1, CSD was directed towards an increase in negative mental states. In study 2, the CSD direction matched the direction of symptom shifts in 34 individuals. The accuracy of the indicator was higher in subsets of individuals with larger absolute symptom transitions. The indicator’s accuracy exceeded chance levels in sensitivity analyses (accuracy 22.92% vs. 11.76%, z=-2.04, P=.02) but not in main analyses (accuracy 27.87% vs. 20.63%, z=-1.32, P=.09).ConclusionsThe CSD direction may predict whether upcoming symptom transitions involve remission or worsening. However, this may only hold for specific individuals, namely those with large symptom transitions. Future research is needed to replicate these findings and to delineate for whom CSD reliably forecasts the direction of impending symptom transitions.

A comparison and classification of oscillatory characteristics in speech perception and covert speech

Covert speech, the mental imagery of speaking, has been studied increasingly to understand and decode thoughts in the context of brain-computer interfaces. In studies of speech comprehension, neural oscillations are thought to play a key role in the temporal encoding of speech. However, little is known about the role of oscillations in covert speech. In this study, we investigated the oscillatory involvements in covert speech and speech perception. Data were collected from 10 participants with 64 channel EEG. Participants heard the words, ‘blue’ and ‘orange’, and subsequently mentally rehearsed them. First, continuous wavelet transform was performed on epoched signals and subsequently two-tailed t-tests between two classes (tasks) were conducted to determine statistical differences in frequency and time (t-CWT). In the current experiment, a task comprised speech perception or covert rehearsal of a word while a condition was the discrimination between tasks. Features were extracted using t-CWT and subsequently classified using a support vector machine. θ and γ phase amplitude coupling (PAC) was also assessed within tasks and across conditions between perception and covert activities (i.e. cross-task). All binary classifications accuracies (80–90%) significantly exceeded chance level, supporting the use of t-CWT in determining relative oscillatory involvements. While the perception condition dynamically invoked all frequencies with more prominent θ and α activity, the covert condition favoured higher frequencies with significantly higher γ activity than perception. Moreover, the perception condition produced significant θ-γ PAC, possibly corroborating a reported linkage between syllabic and phonemic sampling. Although this coupling was found to be suppressed in the covert condition, we found significant cross-task coupling between perception θ and covert speech γ. Covert speech processing appears to be largely associated with higher frequencies of EEG. Importantly, the significant cross-task coupling between speech perception and covert speech, in the absence of within-task covert speech PAC, seems to support the notion that the γ- and θ-bands reflect, respectively, shared and unique encoding processes across tasks.

Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

BackgroundPatients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting. MethodsThis study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation). ResultsFND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%). ConclusionsThe results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

Immediate Post-Concussion and Cognitive Testing (ImPACT): Effects of Data Integration Strategies on Classification Accuracy.

The current study aimed to evaluate varying data integration procedures and their effects on the classification accuracy of Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT). Data were collected from an independent secondary school that included students in grades 9 to 12. The study examined 300 consecutive ImPACT score reports generated by secondary school students between 2010 and 2015. To appraise ImPACT's utility as a serialized measure, standardized regression-based equations were formulated to compute reliable change index scores. Discriminant function analyses (DFAs) consisting of varying combinations of ImPACT composite scores were conducted and their accuracy was compared to that produced by the standard interpretive procedure. Varying combinations of scores produced on Verbal Memory, Visual Memory, Visual Motor Speed, Reaction Speed, and the Postconcussion Symptom Scale were included in analyses. DFAs yielded sensitivities ranging from 31% to 49%, specificities from 88% to 95%, positive predictive values (PPVs) from 61% to 83%, and negative predictive values (NPVs) from 67% to 75%. Conversely, the standard interpretive procedure yielded a sensitivity of 73%, specificity of 43%, PPV of 45%, and NPV of 72%. The standard interpretive procedure produced a higher sensitivity than the DFAs; however, its PPV did not exceed chance levels. Conversely, DFA equations produced superior PPVs; however, their sensitivity hovered around 50%, leaving a substantial proportion of individuals with concussion undetected. Cognitive composite scores did not appear to offer significant incremental utility in relation to symptom self-report. Base rate conditions and psychometric factors appeared to contribute to ImPACT's limited classification accuracy.

Clinical predictors of treatment response towards exposure therapy in virtuo in spider phobia: A machine learning and external cross-validation approach

While being highly effective on average, exposure-based treatments are not equally effective in all patients. The a priori identification of patients with a poor prognosis may enable the application of more personalized psychotherapeutic interventions. We aimed at identifying sociodemographic and clinical pre-treatment predictors for treatment response in spider phobia (SP).N = 174 patients with SP underwent a highly standardized virtual reality exposure therapy (VRET) at two independent sites. Analyses on group-level were used to test the efficacy. We applied a state-of-the-art machine learning protocol (Random Forests) to evaluate the predictive utility of clinical and sociodemographic predictors for a priori identification of individual treatment response assessed directly after treatment and at 6-month follow-up. The reliability and generalizability of predictive models was tested via external cross-validation.Our study shows that one session of VRET is highly effective on a group-level and is among the first to reveal long-term stability of this treatment effect. Individual short-term symptom reductions could be predicted above chance, but accuracies dropped to non-significance in our between-site prediction and for predictions of long-term outcomes. With performance metrics hardly exceeding chance level and the lack of generalizability in the employed between-site replication approach, our study suggests limited clinical utility of clinical and sociodemographic predictors. Predictive models including multimodal predictors may be more promising.

Multimodal coordination of vocal and gaze behavior in mother-infant dyads across the first year of life.

Research examining mother-infant interactions indicates a close connection between vocal and gaze behavior. The present longitudinal study examined the development of both intraindividual and dyadic coordination of vocal and gaze behavior in mother-infant dyads at 3, 6, 9, and 12months. Mother and infant vocalization and gaze behavior during in-home toy play interactions were coded on a moment-by-moment basis and coordinations (i.e., co-occurrences and sequences of behavior) were compared to randomized baselines in order to determine whether coordinate exceeded chance levels. Infants timed their own vocalizations with gaze to partner's face and inhibited vocalizations during gaze to objects at greater than chance levels across the first year. Mother's displayed above-chance intraindividual coordination of vocalizations and gaze to partner's face and objects. Mothers and infants demonstrated dyadic coordination of vocalizations and gaze at above-chance levels, but developmental change and leading-following dynamics varied based on gaze location (i.e., face vs. object). Results emphasize the importance of examining coordination across communication modalities and of considering bidirectional influences on mother and infant vocal and gaze behavior.

Cultural norms influence nonverbal emotion communication: Japanese vocalizations of socially disengaging emotions.

Nonverbal vocalizations of some emotions have been found to be recognizable both within and across cultures. However, East Asians tend to suppress socially disengaging emotions because of interdependent views on self-other relationships. Here we tested the possibility that norms in interdependent cultures around socially disengaging emotions may influence nonverbal vocal communication of emotions. Specifically, we predicted that East Asians' vocalizations of socially disengaging emotions would be less recognizable to Westerners than those of other emotions. To test this hypothesis, we performed a balanced cross-cultural experiment in which 30 Dutch and 30 Japanese listeners categorized and rated Dutch and Japanese vocalizations expressing nine emotions including anger and triumph, two socially disengaging emotions. The only condition for which recognition performance failed to exceed chance level was Dutch listeners' judgments of Japanese anger vocalizations, p = .302. The magnitude of the in-group advantage (i.e., enhanced recognition accuracy when producer and perceiver cultures match) was also largest for Japanese anger vocalizations out of all the 18 conditions investigated, p < .001. The second largest in-group advantage was obtained for Japanese triumph vocalizations, p < .001. In addition, Dutch listeners rated Japanese vocalizations of anger and triumph as less intense, negative/positive, and aroused than did Japanese listeners, ps < .001. Taken together, these findings suggest that East Asian-specific cultural norms of interpersonal relationships are associated with specificity in nonverbal vocal communication of socially disengaging emotions, especially anger, to the point that some signals can only be understood by individuals who are culturally familiar with them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging.

The exact neurobiological underpinnings of gender identity (i.e., the subjective perception of oneself belonging to a certain gender) still remain unknown. Combining both resting-state functional connectivity and behavioral data, we examined gender identity in cisgender and transgender persons using a data-driven machine learning strategy. Intrinsic functional connectivity and questionnaire data were obtained from cisgender (men/women) and transgender (trans men/trans women) individuals. Machine learning algorithms reliably detected gender identity with high prediction accuracy in each of the four groups based on connectivity signatures alone. The four normative gender groups were classified with accuracies ranging from 48% to 62% (exceeding chance level at 25%). These connectivity-based classification accuracies exceeded those obtained from a widely established behavioral instrument for gender identity. Using canonical correlation analyses, functional brain measurements and questionnaire data were then integrated to delineate nine canonical vectors (i.e., brain-gender axes), providing a multilevel window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguishable brain phenotypes for gender identity, advocating a biologically grounded reconceptualization of gender dimorphism. We hope to pave the way towards objective, data-driven diagnostic markers for gender identity and transgender, taking into account neurobiological and behavioral differences in an integrative modeling approach.

Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach

The availability of large-scale datasets and sophisticated machine learning tools enables developing models that predict treatment outcomes for individual patients. However, few studies used routinely available sociodemographic and clinical data for this task, and many previous investigations used highly selected samples. This study aimed to investigate cognitive behavioral therapy (CBT) outcomes in a large, naturalistic and longitudinal dataset. Routine data from a university-based outpatient center with n = 2.147 patients was analyzed. Only baseline data including sociodemographics, symptom measures and functional impairment ratings was used for prediction. Different competing classification and regression models were compared to each other; the best models were then applied to previously unseen validation data. Applied on the validation set, the best performing classification model for remission achieved a balanced accuracy of 59% (p < 0.001) and the best performing regression model for dimensional change achieved r = 0.27 (p < 0.001). Age, sex, functional impairment, symptom severity, and axis II comorbidity were among the most important features. Predictor performances significantly exceeded chance level but were far from clinical utility. Neither applying more sophisticated approaches nor restricting the sample to homogeneous subgroups resulted in considerable performance gains. Adding hypotheses-based, more specific clinical constructs and deep (e.g. neurobiological) to digital phenotypes may increase prediction performance.

Temporal-order information can be maintained in non-conscious working memory

Classical theories hold conscious perception and working memory to be tightly interwoven. Recent work has challenged this assumption, demonstrating that information may be stored for several seconds without any subjective awareness. Does such non-conscious working memory possess the same functional properties as conscious working memory? Here, we probe whether non-conscious working memory can maintain multiple items and their temporal order. In a visual masking task with a delayed response, 38 participants were asked to retain the location and order of presentation of two sequentially flashed spatial positions, and retrieve both after a 2.5 second delay. Even when subjective visibility was nil, subjects’ objective forced-choice performance exceeded chance level and, crucially, distinct retrieval of the first and second location was observed on both conscious and non-conscious trials. Non-conscious working memory may therefore store two items in proper temporal order. These findings can be explained by recent models of activity-silent working memory.

Monitoring Pilot's Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions.

Recent technological progress has allowed the development of low-cost and highly portable brain sensors such as pre-amplified dry-electrodes to measure cognitive activity out of the laboratory. This technology opens promising perspectives to monitor the “brain at work” in complex real-life situations such as while operating aircraft. However, there is a need to benchmark these sensors in real operational conditions. We therefore designed a scenario in which twenty-two pilots equipped with a six-dry-electrode EEG system had to perform one low load and one high load traffic pattern along with a passive auditory oddball. In the low load condition, the participants were monitoring the flight handled by a flight instructor, whereas they were flying the aircraft in the high load condition. At the group level, statistical analyses disclosed higher P300 amplitude for the auditory target (Pz, P4 and Oz electrodes) along with higher alpha band power (Pz electrode), and higher theta band power (Oz electrode) in the low load condition as compared to the high load one. Single trial classification accuracy using both event-related potentials and event-related frequency features at the same time did not exceed chance level to discriminate the two load conditions. However, when considering only the frequency features computed over the continuous signal, classification accuracy reached around 70% on average. This study demonstrates the potential of dry-EEG to monitor cognition in a highly ecological and noisy environment, but also reveals that hardware improvement is still needed before it can be used for everyday flight operations.

Are young children’s preferences and evaluations of moral and conventional transgressors associated with domain distinctions in judgments?

The current study investigated associations between children’s preferences and evaluations of moral and social-conventional transgressors in a novel puppet task and their links with explicit judgments in a standard interview. Children aged 2–3.25 years (M = 2.53 years, SD = 0.35) and 3.5–5 years (M = 4.38 years, SD = 0.52) watched two pairs of live puppet shows depicting actors committing a moral transgression and a conventional transgression and chose which transgressor they liked more, preferred more as a friend, thought was more wrong, and should get in more trouble; they also distributed resources to the transgressors. At both ages, children allocated fewer resources to moral transgressors than to conventional transgressors, but younger children’s other responses did not exceed chance levels. In contrast, older children chose the moral transgressor as more wrong, more deserving of punishment, and less likeable. Preferences were associated with evaluations in the puppet task, particularly among older children. In contrast, all children differentiated between moral and conventional transgressions in their explicit judgments, with age differences found only in rule independence. More mature moral judgments, as assessed by latent difference scores reflecting moral–conventional distinctions, were associated with preferring to befriend the conventional transgressor and evaluating the moral transgressor as more wrong. Together, these results show age-related increases in children’s moral understanding of—and stronger associations between—preferences and evaluations with age.

Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans.

Seeing an action may activate the corresponding action motor code in the observer. It remains unresolved whether seeing and performing an action activates similar action-specific motor codes in the observer and the actor. We used novel hyperclassification approach to reveal shared brain activation signatures of action execution and observation in interacting human subjects. In the first experiment, two "actors" performed four types of hand actions while their haemodynamic brain activations were measured with 3-T functional magnetic resonance imaging (fMRI). The actions were videotaped and shown to 15 "observers" during a second fMRI experiment. Eleven observers saw the videos of one actor, and the remaining four observers saw the videos of the other actor. In a control fMRI experiment, one of the actors performed actions with closed eyes, and five new observers viewed these actions. Bayesian canonical correlation analysis was applied to functionally realign observers' and actors' fMRI data. Hyperclassification of the seen actions was performed with Bayesian logistic regression trained on actors' data and tested with observers' data. Without the functional realignment, between-subjects accuracy was at chance level. With the realignment, the accuracy increased on average by 15 percentage points, exceeding both the chance level and the accuracy without functional realignment. The highest accuracies were observed in occipital, parietal and premotor cortices. Hyperclassification exceeded chance level also when the actor did not see her own actions. We conclude that the functional brain activation signatures underlying action execution and observation are partly shared, yet these activation signatures may be anatomically misaligned across individuals.

EEG Classification of Covert Speech Using Regularized Neural Networks

Communication using brain–computer interfaces (BCIs) can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography (EEG) signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words “yes” or “no.” A multilayer perceptron (MLP) artificial neural network (ANN) was used to classify all three pairwise combinations of “yes,” “no,” and rest trials and also for ternary classification. An average accuracy of 75.7% ± 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level (57.8%). The classification of “yes” versus “no” yielded an average accuracy of 63.2 ± 6.4 with ten participants surpassing chance level (57.8%). Finally, the ternary classification yielded an average accuracy of 54.1% ± 9.7 with all participants exceeding chance level (39.1%). The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.