EEG study of facial emotion recognition in the fathers of autistic children

Autistic traits shape neuronal oscillations during emotion perception under attentional load modulation

Emotional content is particularly salient, but situational factors such as cognitive load may disturb the attentional prioritization towards affective stimuli and interfere with their processing. In this study, 31 autistic and 31 typically developed children volunteered to assess their perception of affective prosodies via event-related spectral perturbations of neuronal oscillations recorded by electroencephalography under attentional load modulations induced by Multiple Object Tracking or neutral images. Although intermediate load optimized emotion processing by typically developed children, load and emotion did not interplay in children with autism. Results also outlined impaired emotional integration emphasized in theta, alpha and beta oscillations at early and late stages, and lower attentional ability indexed by the tracking capacity. Furthermore, both tracking capacity and neuronal patterns of emotion perception during task were predicted by daily-life autistic behaviors. These findings highlight that intermediate load may encourage emotion processing in typically developed children. However, autism aligns with impaired affective processing and selective attention, both insensitive to load modulations. Results were discussed within a Bayesian perspective that suggests atypical updating in precision between sensations and hidden states, towards poor contextual evaluations. For the first time, implicit emotion perception assessed by neuronal markers was integrated with environmental demands to characterize autism.

Deep learning-based EEG emotion recognition: Current trends and future perspectives

Automatic electroencephalogram (EEG) emotion recognition is a challenging component of human-computer interaction (HCI). Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. This paper aims to provide an up-to-date and comprehensive survey of EEG emotion recognition, especially for various deep learning techniques in this area. We provide the preliminaries and basic knowledge in the literature. We review EEG emotion recognition benchmark data sets briefly. We review deep learning techniques in details, including deep belief networks, convolutional neural networks, and recurrent neural networks. We describe the state-of-the-art applications of deep learning techniques for EEG emotion recognition in detail. We analyze the challenges and opportunities in this field and point out its future directions.

Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition

FACIAL expression is one of the most efficient, universal and fundamental indicators to identify their emotions and intentions in humans. Various experiments have already been performed on automatic Facial Emotion Recognition (FER) owing to useful significance in medical diagnosis, stress monitoring for drivers, sociable robots, and other human-computer interface devices. Here, this proposed framework consists of two processes namely; “(i) proposed feature extraction and (ii) classification”. Here, a major novelty relies in the initial phase (i.e. feature extraction phase), where the Proposed Local Vector Pattern (Proposed- LVP) based features are extracted. In addition to the proposed-LVP, the other Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) based features are also extracted. Besides, the Principal Component Analysis (PCA) method is used for reducing the dimension of the features. Further, they are subjected to classification process, where Optimized Neural Network (NN) is used. More particularly, a new Improved Elephant Herding Optimization (EHO) model termed as Self Adaptive-EHO (SA-EHO) is used to train the NN model via selecting the optimal weights. At last, the proposed work performance is computed over the other traditional systems with respect to the positive measures like “accuracy, sensitivity, specificity and precision”; negative measures like “False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR)”; other measures like “Negative Predictive Value (NPV), F1-score and Matthew’s Correlation Coefficient (MCC)”, respectively.

Mediating Effect of Emotional and Social Competences on Interrelations Between Gender, Age and the Broad Autism Phenotype

The study aimed to identify the mediating role of emotional intelligence and social competences in the relationship between gender and broad autism phenotype (BAP) as well as between age and BAP. It comprised 85 parents of children with ASD. They completed the questionnaires of Autism-Spectrum Quotient, social competences, and emotional intelligence. The results revealed that emotional intelligence in general and its two dimensions: the ability to accept and express emotions and to empathize are important mediators of the relationship between gender and BAP. Also, social competences in general and two of their dimensions: the effectiveness of behavior in intimate situations and in social situations were significant mediators. The relationship between age and BAP could not be confirmed.