Facial Emotion Recognition Task Research Articles

Effects of Sleep Quality, Acute Sleep Deprivation, and Napping on Facial Emotion Recognition Accuracy and Speed.

To investigate the effects of sleep quality, sleep deprivation, and napping on facial emotion recognition (FER) accuracy and speed. This research included a cross-sectional study (102 qualified participants) and a randomized controlled study (26 in the napping group and 24 in the control group). The stimuli for the FER task were obtained from the Chinese Facial Affective Picture System (CFAPS). Four facial expressions (fearful, disgusted, sad, and angry) were used. The Pittsburgh Sleep Quality Index (PSQI), Self-Rating Anxiety Scale, and Self-Rating Depression Scale were used to measure participants' sleep quality and psychological conditions. In Study 1, FER ability was compared between good and poor sleepers. In Study 2, all participants were sleep-deprived for one night, and completed the FER task before and after sleep deprivation. After different interventions (ie, napping for one hour, or walking around for ten minutes), the participants completed the third FER task. Study 1: Poor sleepers were able to recognize sad expressions more accurately compared with good sleepers. Study 2: 30-h sleep deprivation had no significant effect on the accuracy (ACC). Napping after sleep deprivation improved the FER ACC of upper-face expressions and marginally significantly improved the FER ACC of disgusted expressions. Better sleep quality was linked to lower FER accuracy, particularly in recognizing sad expressions, while no significant differences in recognition speed were observed. Additionally, 30hours of sleep deprivation did not affect FER accuracy, but napping after sleep deprivation improved accuracy for upper-face and marginally for disgusted expressions.

Road lighting for pedestrians: Comparing full-face and 3/4 views in a facial emotion recognition task

Road lighting should support the needs of pedestrians to make interpersonal evaluations after dark, for example, whether it feels safe to walk towards the person ahead or if avoiding action should be taken. In previous studies this has been investigated using a facial emotion recognition (FER) task, but using only full-face views of the person ahead. Other views are possible and this might affect the ability to make FER judgements and thus the impact of changes in light level. Reported here are the results of an experiment investigating FER with full-face and 3/4 views. The results show that while correct expression recognition is reduced with the 3/4 view, there is no interaction between face luminance and face view.

Individual differences in interoception and autistic traits share altered facial emotion perception, but not recognition per se

While alterations in both physiological responses to others’ emotions as well as interoceptive abilities have been identified in autism, their relevance in altered emotion recognition is largely unknown. We here examined the role of interoceptive ability, facial mimicry, and autistic traits in facial emotion processing in non-autistic individuals. In an online Experiment 1, participants (N = 99) performed a facial emotion recognition task, including ratings of perceived emotional intensity and confidence in emotion recognition, and reported on trait interoceptive accuracy, interoceptive sensibility and autistic traits. In a follow-up lab Experiment 2 involving 100 participants, we replicated the online experiment and additionally investigated the relationship between facial mimicry (measured through electromyography), cardiac interoceptive accuracy (evaluated using a heartbeat discrimination task), and autistic traits in relation to emotion processing. Across experiments, neither interoception measures nor facial mimicry accounted for a reduced recognition of specific expressions with higher autistic traits. Higher trait interoceptive accuracy was rather associated with more confidence in correct recognition of some expressions, as well as with higher ratings of their perceived emotional intensity. Exploratory analyses indicated that those higher intensity ratings might result from a stronger integration of instant facial muscle activations, which seem to be less integrated in intensity ratings with higher autistic traits. Future studies should test whether facial muscle activity, and physiological signals in general, are correspondingly less predictive of perceiving emotionality in others in individuals on the autism spectrum, and whether training interoceptive abilities might facilitate the interpretation of emotional expressions.

Deficits of Facial Emotion Recognition in Elderly Individuals with Mild Cognitive Impairment.

The study of facial emotion recognition is under-explored in subjects with mild cognitive impairment (MCI). We investigated whether deficits in facial emotion recognition are present in patients with MCI. We also analyzed the relationship between facial emotion recognition and different domains of cognitive function. This study included 300 participants aged 60 years or older with cognitive decline. We evaluated 181 MCI and 119 non-MCI subjects using the Seoul Neuropsychological Screening Battery-Core (SNSB-C) and facial emotion recognition task using six facial expressions (anger, disgust, fear, happiness, sadness and surprise). A Generalized Linear Model (GLM) was used to assess the association between cognitive performance and accuracy of facial emotion recognition and to compare facial emotion recognition in the MCI group based on the impairment of five different domains of cognitive function. The model was adjusted for age, sex, years of education, and depressive symptoms. Patients with MCI had a lower score for accurately recognizing total facial emotion (0.48 vs. 0.53; ρ = 0.0003) and surprise (0.73 vs. 0.81; ρ = 0.0215) when compared to cognitively healthy subjects. We also discovered that frontal/executive function domain (Digit Symbol Coding [DSC, 0.38 vs. 0.49; p < 0.0001], Controlled Oral Word Association Test [COWAT, 0.42 vs. 0.49; p = 0.0001], Korean-Trail Making Test [K-TMT, 0.37 vs. 0.48; p = 0.0073], Korean-Color Word Stroop Test [K-CWST, 0.43 vs. 0.49; p = 0.0219]) and language domain (Korean-Boston Naming Test [S-K-BNT, 0.46 vs. 0.47; p = 0.003]) were statistically associated with the deficits of facial emotion recognition in patients with MCI. We observed a significant association between deficits in facial emotion recognition and cognitive impairment in elderly individuals.

Impact of psychosocial stress on facial emotion recognition in schizophrenia and controls: an experimental study in a forensic sample.

Psychotic disorders have been associated with dysregulated stress reactions and adaptation. Little is known about the neuroendocrine responses to psychosocial stress in justice-involved individuals with schizophrenia. Using an experimental research design, the present study aims to examine differences in the subjective and neuroendocrine responses to psychosocial stress and its impact on facial emotion recognition (FER) and performance on an arithmetic task in chronically ill justice-involved individuals with schizophrenia (PAT) and a healthy control group. PAT undergoing treatment in forensic psychiatric inpatient wards (n = 17) and a healthy control group (n = 17) were assessed regarding sociodemographic and clinical characteristics. Additionally, salivary cortisol levels, measured before and after performing a psychosocial stress task [Montreal Imaging Stress Task (MIST)], and performance on an arithmetic problem-solving task and two FER tasks were recorded. Two participants dropped out, one from each group. Therefore, the final sample consisted of 32 individuals. Significant group differences in FER were recorded. There was a significant rise in subjective perception of momentary strain relating to the induction of psychosocial stress in both groups. Notably, the pre-stress level of subjective strain was higher in the PAT group than controls. Acute psychosocial stress induced an increase in FER performance in a sub-task related to naming emotions in individuals with schizophrenia spectrum disorder. The results underline the importance of psychosocial and therapeutic interventions aimed at strengthening stress resilience in individuals with schizophrenia spectrum disorders.

Transfer Learning for Facial Emotion Recognition on Small Datasets

In the context of human interactions, complexity science (CS) provides a way to understand the dynamics that arise from the interplay of different individuals. Recently, the possibility of applying the theory of CS to computer science, has shifted the focus to the research of machine learning methods for studying human behaviors and relational dynamics. Among the various existing AI techniques, facial emotion recognition (FER) has proven to be the best-performing and easiest to use human- computer interaction (HCI) tool for emotion detection. Despite the numerous existing approaches, the task of FER is not trivial, mainly due to the absence of large enough datasets to train deep learning (DL) models. A widely used solution is transfer learning (TL), which allows a model, pre-trained on enough data, to be used for a specific task where there is much less data available. The aim of our work is to test the effectiveness of TL for FER on an extremely small dataset, to understand which parameters need to be optimised to obtain the best outcomes. The results showed an overall accuracy of 85.54% for our model and revealed the concrete possibility of applying computer science to complex systems typical of the human psyche.

Effectiveness of Group-Play Training on Emotion Recognition for Young Children Who Are Deaf or Hard of Hearing

Abstract: Deaf and hard of hearing (DHH) children are developmentally delayed in facial emotion recognition (FER). This study aimed to explore the possibility of enhancing the FER ability of DHH preschoolers through a group-play intervention. Nineteen children with a cochlear implant or hearing aid were enrolled in a 4-week intervention; six DHH children were assigned to a control group. The training program included a learning procedure for four basic emotions: happiness, sadness, anger, and fear. A pretest/posttest design was used to measure the DHH children's FER performance. The results indicated that although the two groups performed comparably on the FER task before the training (pretest), the performance of the intervention group was significantly better than that of the control group in the posttest. Moreover, the DHH children in the intervention group showed the greatest improvement in FER of happiness.

A study on expression recognition based on improved mobilenetV2 network

This paper proposes an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. The network inherits the characteristics of MobilenetV2 depthwise separated convolution, signifying a reduction in computational load while maintaining a lightweight profile. It utilizes a reverse fusion mechanism to retain negative features, which makes the information less likely to be lost. The SELU activation function is used to replace the RELU6 activation function to avoid gradient vanishing. Meanwhile, to improve the feature recognition capability, the channel attention mechanism (Squeeze-and-Excitation Networks (SE-Net)) is integrated into the MobilenetV2 network. Experiments conducted on the facial expression datasets FER2013 and CK + showed that the proposed network model achieved facial expression recognition accuracies of 68.62% and 95.96%, improving upon the MobileNetV2 model by 0.72% and 6.14% respectively, and the parameter count decreased by 83.8%. These results empirically verify the effectiveness of the improvements made to the network model.

Congenital Anosmia and Facial Emotion Recognition

Major functions of the olfactory system include guiding ingestion and avoidance of environmental hazards. People with anosmia report reliance on others, for example to check the edibility of food, as their primary coping strategy. Facial expressions are a major source of non-verbal social information that can be used to guide approach and avoidance behaviour. Thus, it is of interest to explore whether a life-long absence of the sense of smell heightens sensitivity to others’ facial emotions, particularly those depicting threat. In the present, online study 28 people with congenital anosmia (mean age 43.46) and 24 people reporting no olfactory dysfunction (mean age 42.75) completed a facial emotion recognition task whereby emotionally neutral faces (6 different identities) morphed, over 40 stages, to express one of 5 basic emotions: anger, disgust, fear, happiness, or sadness. Results showed that, while the groups did not differ in their ability to identify the final, full-strength emotional expressions, nor in the accuracy of their first response, the congenital anosmia group successfully identified the emotions at significantly lower intensity (i.e. an earlier stage of the morph) than the control group. Exploratory analysis showed this main effect was primarily driven by an advantage in detecting anger and disgust. These findings indicate the absence of a functioning sense of smell during development leads to compensatory changes in visual, social cognition. Future work should explore the neural and behavioural basis for this advantage.

Dynamic facial emotion recognition and affective prosody recognition are associated in patients with temporal lobe epilepsy

Deficits in facial emotion recognition have frequently been established in temporal lobe epilepsy (TLE). However, static, rather than dynamic emotion recognition paradigms have been applied. Affective prosody has been insufficiently studied in TLE, and there is a lack of studies investigating associations between auditory and visual emotion recognition. We wished to investigate potential deficits in a dynamic morph task of facial emotion recognition and in an affective prosody recognition task, as well as associations between both tasks. 25 patients with TLE and 24 healthy controls (CG) performed a morph task with faces continuously changing in their emotional intensity. They had to press a button, as soon as they were able to recognize the emotion expressed, and label it accordingly. In the auditory task, subjects listened to neutral sentences spoken in varying emotional tones, and labeled the emotions. Correlation analyses were conducted across both tasks. TLE patients showed significantly reduced prosody recognition compared to CG, and in the morph task, there was a statistical trend towards significantly reduced performance for TLE. Recognition rates in both tasks were significantly associated. TLE patients show deficits in affective prosody recognition, and they may also be impaired in a morph task with dynamically changing facial expressions. Impairments in basic social-cognitive tasks in TLE seem to be modality-independent.

Disrupted properties of functional brain networks in major depressive disorder during emotional face recognition: an EEG study via graph theory analysis.

Previous neuroimaging studies have revealed abnormal brain networks in patients with major depressive disorder (MDD) in emotional processing. While any cognitive task consists of a series of stages, little is yet known about the topology of functional brain networks in MDD for these stages during emotional face recognition. To address this problem, electroencephalography (EEG)-based functional brain networks of MDD patients at different stages of facial information processing were investigated in this study. First, EEG signals were collected from 16 patients with MDD and 18 age-, gender-, and education-matched normal subjects when performing an emotional face recognition task. Second, the global field power (GFP) method was employed to divide group-averaged event-related potentials into different stages. Third, using the phase transfer entropy (PTE) approach, the brain networks of MDD patients and normal individuals were constructed for each stage in negative and positive face processing, respectively. Finally, we compared the topological properties of brain networks of each stage between the two groups using graph theory approaches. The results showed that the analyzed three stages of emotional face processing corresponded to specific neurophysiological phases, namely, visual perception, face recognition, and emotional decision-making. It was also demonstrated that depressed patients showed abnormally decreased characteristic path length at the visual perception stage of negative face recognition and normalized characteristic path length in the stage of emotional decision-making during positive face processing compared to healthy subjects. Furthermore, while both the MDD and normal groups' brain networks were found to exhibit small-world network characteristics, the brain network of patients with depression tended to be randomized. Moreover, for patients with MDD, the centro-parietal region may lose its status as a hub in the process of facial expression identification. Together, our findings suggested that altered emotional function in MDD patients might be associated with disruptions in the topological organization of functional brain networks during emotional face recognition, which further deepened our understanding of the emotion processing dysfunction underlying MDD.

Aberrant high-beta band functional connectivity during reward processing in melancholic major depressive disorder: An MEG study

ObjectiveTo identify the spatial–temporal pattern variation of whole-brain functional connectivity (FC) during reward processing in melancholic major depressive disorder (MDD) patients, and to determine the clinical correlates of connectomic differences. Methods61 MDD patients and 32 healthy controls were enrolled into the study. During magnetoencephalography (MEG) scanning, all participants completed the facial emotion recognition task. The MDD patients were further divided into two groups: melancholic (n = 31) and non-melancholic (n = 30), based on the Mini International Neuropsychiatric Interview (M.I.N.I.) assessment. Melancholic symptoms were examined by using the 6-item melancholia subscale from the Hamilton Depression Rating Scale (HAM-D6). The whole-brain orthogonalized power envelope connections in the high-beta band (20–35 Hz) were constructed in each period after the happy emotional stimuli (0–200 ms, 100–300 ms, 200–400 ms, 300–500 ms, and 400–600 ms). Then, the network-based statistic (NBS) was used to determine the specific abnormal connection patterns in melancholic MDD patients. ResultsThe NBS identified a sub-network difference at the mid-late period (300–500 ms) in response to happy faces among the three groups (corrected P = 0.035). Then, the post hoc and correlation analyses found five FCs were decreased in melancholic MDD patients and were related to HAM-D6 score, including FCs of left fusiform gyrus-right orbital inferior frontal gyrus (r = −0.52, P < 0.001), left fusiform gyrus-left amygdala (r = −0.26, P = 0.049), left posterior cingulate gyrus-right precuneus (r = −0.32, P = 0.025), left precuneus-right precuneus (r = −0.27, P = 0.049), and left precuneus-left inferior occipital gyrus (r = −0.32, P = 0.025). ConclusionIn response to happy faces, melancholic MDD patients demonstrated a disrupted functional connective pattern (20–35 Hz, 300–500 ms), which involved brain regions in visual information processing and the limbic system. The aberrant functional connective pattern in reward processing might be a biomarker of melancholic MDD.

Deep Learning Approaches for Classification of Emotion Recognition based on Facial Expressions

Automated emotion recognition (AEE) plays a crucial role in numerous industries that depend on understanding human emotional responses, such as advertising, technology, and human-robot interaction, particularly within the Information Technology (IT) field. However, current systems can often come up short in comprehensively understanding an individual's emotions, as prior research has mainly focused on assessing facial expressions and categorizing them into seven primary emotions, including neutrality. In this study, we present several Deep Convolutional Neural Network (CNN) models designed specifically for the task of facial emotion recognition, utilizing the FER2013 and RAF datasets. The baseline CNN model is established through a trial-and-error method, and its results are compared with more complex deep learning techniques, including ResNet18, VGGNet16, VGGNet19, and Efficient Net-B0 models. Among these models, the VGGNet19 model achieved the best results with a test accuracy of 71.02% on the FER2013 dataset. In comparison, the ResNet18 model outperformed all other models with an 86.02% test accuracy on the RAF-DB dataset. These results underscore the potential for advancing automated emotion recognition through complex deep-learning techniques.

Multi-source transfer learning for facial emotion recognition using multivariate correlation analysis.

Deep learning techniques have proven to be effective in solving the facial emotion recognition (FER) problem. However, it demands a significant amount of supervision data which is often unavailable due to privacy and ethical concerns. In this paper, we present a novel approach for addressing the FER problem using multi-source transfer learning. The proposed method leverages the knowledge from multiple data sources of similar domains to inform the model on a related task. The approach involves the optimization of aggregate multivariate correlation among the source tasks trained on the source dataset, thus controlling the transfer of information to the target task. The hypothesis is validated on benchmark datasets for facial emotion recognition and image classification tasks, and the results demonstrate the effectiveness of the proposed method in capturing the group correlation among features, as well as being robust to negative transfer and performing well in few-shot multi-source adaptation. With respect to the state-of-the-art methods MCW and DECISION, our approach shows an improvement of 7% and [Formula: see text]15% respectively.

Supraphysiological testosterone levels from anabolic steroid use and reduced sensitivity to negative facial expressions in men

RationaleAnabolic-androgenic steroids (AAS) are used to improve physical performance and appearance, but have been associated with deficits in social cognitive functioning. Approximately 30% of people who use AAS develop a dependence, increasing the risk for undesired effects.ObjectivesTo assess the relationship between AAS use (current/previous), AAS dependence, and the ability to recognize emotional facial expressions, and investigate the potential mediating role of hormone levels.MethodsIn total 156 male weightlifters, including those with current (n = 45) or previous (n = 34) AAS use and never-using controls (n = 77), completed a facial Emotion Recognition Task (ERT). Participants were presented with faces expressing one out of six emotions (sadness, happiness, fear, anger, disgust, and surprise) and were instructed to indicate which of the six emotions each face displayed. ERT accuracy and response time were recorded and evaluated for association with AAS use status, AAS dependence, and serum reproductive hormone levels. Mediation models were used to evaluate the mediating role of androgens in the relationship between AAS use and ERT performance.ResultsCompared to never-using controls, men currently using AAS exhibited lower recognition accuracy for facial emotional expressions, particularly anger (Cohen’s d = −0.57, pFDR = 0.03) and disgust (d = −0.51, pFDR = 0.05). Those with AAS dependence (n = 47) demonstrated worse recognition of fear relative to men without dependence (d = 0.58, p = 0.03). Recognition of disgust was negatively correlated with serum free testosterone index (FTI); however, FTI did not significantly mediate the association between AAS use and recognition of disgust.ConclusionsOur findings demonstrate impaired facial emotion recognition among men currently using AAS compared to controls. While further studies are needed to investigate potential mechanisms, our analysis did not support a simple mediation effect of serum FTI.

Effects of two different social exclusion paradigms on ambiguous facial emotion recognition

ABSTRACT Social exclusion is an emotionally painful experience that leads to various alterations in socio-emotional processing. The perceptual and emotional consequences that may arise from experiencing social exclusion can vary depending on the paradigm used to manipulate it. Exclusion paradigms can vary in terms of the severity and duration of the leading exclusion experience, thereby classifying it as either a short-term or long-term experience. The present study aimed to study the impact of exclusion on socio-emotional processing using different paradigms that caused experiencing short-term and imagining long-term exclusion. Ambiguous facial emotions were used as socio-emotional cues. In study 1, the Ostracism Online paradigm was used to manipulate short-term exclusion. In study 2, a new sample of participants imagined long-term exclusion through the future life alone paradigm. Participants of both studies then completed a facial emotion recognition task consisting of morphed ambiguous facial emotions. By means of Point of Subjective Equivalence analyses, our results indicate that the experience of short-term exclusion hinders recognising happy facial expressions. In contrast, imagining long-term exclusion causes difficulties in recognising sad facial expressions. These findings extend the current literature, suggesting that not all social exclusion paradigms affect socio-emotional processing similarly.

Comparative Analysis of Vision Transformer Models for Facial Emotion Recognition Using Augmented Balanced Datasets

Facial emotion recognition (FER) has a huge importance in the field of human–machine interface. Given the intricacies of human facial expressions and the inherent variations in images, which are characterized by diverse facial poses and lighting conditions, the task of FER remains a challenging endeavour for computer-based models. Recent advancements have seen vision transformer (ViT) models attain state-of-the-art results across various computer vision tasks, encompassing image classification, object detection, and segmentation. Moreover, one of the most important aspects of creating strong machine learning models is correcting data imbalances. To avoid biased predictions and guarantee reliable findings, it is essential to maintain the distribution equilibrium of the training dataset. In this work, we have chosen two widely used open-source datasets, RAF-DB and FER2013. As well as resolving the imbalance problem, we present a new, balanced dataset, applying data augmentation techniques and cleaning poor-quality images from the FER2013 dataset. We then conduct a comprehensive evaluation of thirteen different ViT models with these three datasets. Our investigation concludes that ViT models present a promising approach for FER tasks. Among these ViT models, Mobile ViT and Tokens-to-Token ViT models appear to be the most effective, followed by PiT and Cross Former models.

Pseudo-labeling of transfer learning convolutional neural network data for human facial emotion recognition

The relevance of solving the problem of facial emotion recognition on human images in the creation of modern intelligent systems of computer vision and human-machine interaction, online learning and emotional marketing, health care and forensics, machine graphics and game intelligence is shown. Successful examples of technological solutions to the problem of facial emotion recognition using transfer learning of deep convolutional neural networks are shown. But the use of such popular datasets as DISFA, CelebA, AffectNet, for deep learning of convolutional neuralnetworks does not give good results in terms of the accuracy of emotion recognition, because almost all training sets have fundamental flaws related to errors in their creation, such as the lack of data of a certain class, imbalance of classes, subjectivity and ambiguity of labeling, insufficient amount of data for deep learning, etc. It is proposed to overcome the noted shortcomings of popular datasets for emotion recognition by adding to the training sample additional pseudo-labeled images with human emotions, on which recognition occurs with high accuracy. The aim of the research is to increase the accuracy of facial emotion recognitionon the image of a human by developing a pseudo-labeling method for transfer learning of a deep neural network. To achieve the aim, the following tasks were solved: a convolutional neural network model, previously trained on the ImageNet set using the transfer learning method, was adjusted on the RAF-DB data set to solve emotion recognition tasks; a pseudo-labeling method of the RAF−DB set data was developed for semi-supervised learning of a convolutional neural network model for the task of facial emotion recognition; the accuracy of facial emotion recognition was analyzed based on the developed convolutional neural network model and the method of pseudo-labeling of RAF-DB set data for its correction. It is shown that the use of the developed method of pseudo-labeling data and transfer learning of the MobileNet V1 convolutional neural network model allowed to increase the accuracy of facial emotion recognitionon the images of the RAF-DB dataset by 2 percent (from 76 to 78%) according to the F1 estimate. Atthe same time, taking into account the significant imbalance of the classes, for the 7 main emotions in the trainingset, we have a significant increase in the accuracy of recognizing a few representatives of such emotions as surprise (from 71 to 77%), fearful(from 64 to 69%), sad (from 72 to 76%), angrywith (from 64 to 74%), neutral(from 66 to 71%). The accuracy of recognizing the emotion of happy, which is the most common, decreased (from 91 to 86 %) Thus, it can be concluded that the use of the developed pseudo-labeling method gives good results in overcoming such shortcomings of datasets for deep learning of convolutional neural networks such as lack of data of a certain type, imbalance of classes, insufficient amount of data for deep learning, etc.

Sex Differences in Recognition of Face Expressions

Abstract: Emotional facial expressions are a crucial non-verbal communication skill for humans’ interactions. The current study assessed the impact of sex on emotional face recognition. A total of 125 individuals per-formed an online emotional face recognition task. The stimuli were created recutting male and female Saudi volunteers. Results showed that, except of the male participants having faster responses compared to females in general, no main significant differences between the sex group in accuracy nor any significant interaction between participant sex and the sex of the faces. Our findings suggests that the effect of sex on emotional face recognition needs further investigation with well calibrated stimuli. Limitations and the direction of future research in this area were discussed.

Elevated EEG heartbeat-evoked potentials in adolescents with more ADHD symptoms

IntroductionSymptoms of attention deficit hyperactivity disorder (ADHD) are associated with a variety of mental abnormalities, but little is known about the perception and processing of internal signals, i.e., interoception, in individuals with ADHD symptoms. This study aimed to investigate the association between ADHD symptoms and the heartbeat-evoked potential (HEP), known as a neural correlate of automatic interoceptive processing of cardiac signals, in adolescents. MethodsHEPs of 47 healthy adolescent participants (53.2 % female) with a mean age of 14.29 years were measured during an emotional face recognition task. In addition, participants completed a self-report screening for ADHD symptoms. ResultsADHD symptoms were positively related to the HEP activity during the task in three of eight EEG sectors in the left hemisphere, as well as in all sectors in the right hemisphere. DiscussionThis study is the first to demonstrate preliminary a relationship between the strength of HEP activity and ADHD symptoms in awake subjects. This finding of higher HEP amplitudes in subjects with more ADHD symptoms can be interpreted in terms of (i) increased arousal, (ii) altered neural processing of internal processes in an emotion-relevant task, and (iii) a misaligned precision-weighting process of task-irrelevant stimuli according to the predictive coding framework. These different interpretations could be reflected by previous studies showing heterogeneity of psychological deficits in individuals with ADHD symptoms. However, the generalizability to patients with diagnosed ADHD is limited due to the measurement tool for ADHD symptoms and the sample characteristics.