MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG emotion recognition: continuous regression of emotional states and discrete classification of emotions. While classification methods have garnered significant attention, regression methods remain relatively under-explored.

A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face.

Multimodal emotion recognition (MER) refers to the identification and understanding of human emotional states by combining different signals, including-but not limited to-text, speech, and face cues. MER plays a crucial role in the human-computer interaction (HCI) domain. With the recent progression of deep learning technologies and the increasing availability of multimodal datasets, the MER domain has witnessed considerable development, resulting in numerous significant research breakthroughs.

Progressive distribution adapted neural networks for cross-corpus speech emotion recognition.

In this paper, we investigate a challenging but interesting task in the research of speech emotion recognition (SER), i.e., cross-corpus SER.

Adapting Multiple Distributions for Bridging Emotions from Different Speech Corpora.

In this paper, we focus on a challenging, but interesting, task in speech emotion recognition (SER), i.e., cross-corpus SER.

Progressively Discriminative Transfer Network for Cross-Corpus Speech Emotion Recognition.

Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space.

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network.

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals.

Automatic Identification of High-Risk Autism Spectrum Disorder: A Feasibility Study Using Video and Audio Data Under the Still-Face Paradigm.

It is reported that the symptoms of autism spectrum disorder (ASD) could be improved by effective early interventions, which arouses an urgent need for large-scale early identification of ASD. Until now, the screening of ASD has relied on the child psychiatrist to collect medical history and conduct behavioral observations with the help of psychological assessment tools. Such screening measures inevitably have some disadvantages, including strong subjectivity, relying on experts and low-efficiency.

Application of the Still-Face Paradigm in Early Screening for High-Risk Autism Spectrum Disorder in Infants and Toddlers.

Although autism spectrum disorder (ASD) can currently be diagnosed at the age of 2 years, age at ASD diagnosis is still 40 months or even later. In order to early screening for ASD with more objective method, behavioral videos were used in a number of studies in recent years. The still-face paradigm (SFP) was adopted to measure the frequency and duration of non-social smiling, protest behavior, eye contact, social smiling, and active social engagement in high-risk ASD group (HR) and typical development group (TD) (HR: = 45; TD: = 43).

[Characteristics of vocalization in children with autism spectrum disorder during the still-face paradigm].

Objective: To study the characteristics of vocalization during the still-face paradigm (SFP) before the age of 2 years and their correlation with the severity of autism spectrum disorder (ASD) symptoms at diagnosis in children with ASD.

Methods: A total of 43 children aged 7-23 months, who were suspected of ASD, were enrolled as the suspected ASD group, and 37 typical development (TD) children, aged 7-23 months, were enrolled as the TD group. The frequency and durations of vocalization in the SFP were measured.