AIMC Topic: Emotions

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

IEEE journal of biomedical and health informatics
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 ...

Psychology of AI: How AI impacts the way people feel, think, and behave.

Current opinion in psychology
Over the past decade, artificial intelligence (AI) technologies have transformed numerous facets of our lives. In this article, we summarize key themes in emerging AI research in behavioral science. In doing so, we aim to unravel the multifaceted imp...

HiRENet: Novel convolutional neural network architecture using Hilbert-transformed and raw electroencephalogram (EEG) for subject-independent emotion classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Convolutional neural networks (CNNs) are the most widely used deep-learning framework for decoding electroencephalograms (EEGs) due to their exceptional ability to extract hierarchical features from high-dimensional EEG dat...

CKG: Improving ABSA with text augmentation using ChatGPT and knowledge-enhanced gated attention graph convolutional networks.

PloS one
Aspect-level sentiment analysis (ABSA) is a pivotal task within the domain of neurorobotics, contributing to the comprehension of fine-grained textual emotions. Despite the extensive research undertaken on ABSA, the limited availability of training d...

Emotional and cognitive trust in artificial intelligence: A framework for identifying research opportunities.

Current opinion in psychology
This article briefly summarizes trust as a multi-dimensional construct, and trust in AI as a unique but related construct. It argues that because trust in AI is couched within an economic landscape, these two frameworks should be combined to understa...

Image-based facial emotion recognition using convolutional neural network on emognition dataset.

Scientific reports
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limi...

Robots as Mental Health Coaches: A Study of Emotional Responses to Technology-Assisted Stress Management Tasks Using Physiological Signals.

Sensors (Basel, Switzerland)
The current study investigated the effectiveness of social robots in facilitating stress management interventions for university students by evaluating their physiological responses. We collected electroencephalogram (EEG) brain activity and Galvanic...

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.

Journal of affective disorders
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lac...

Sentiment analysis of the Hamas-Israel war on YouTube comments using deep learning.

Scientific reports
Sentiment analysis aims to classify text based on the opinion or mentality expressed in a situation, which can be positive, negative, or neutral. Therefore, in the world, a lot of opinions are available on various social media sites, which must be ga...

Exploring the Use of Natural Language Processing to Understand Emotions of Trainees and Faculty Regarding Entrustable Professional Activity Assessments.

Journal of graduate medical education
In medical education, artificial intelligence techniques such as natural language processing (NLP) are starting to be used to capture and analyze emotions through written text. To explore the application of NLP techniques to understand resident and...