In empirical art research, understanding how viewers judge visual artworks as beautiful is often explored through the study of attributes-specific inherent characteristics or artwork features such as color, complexity, and emotional expressiveness. T...
IEEE transactions on neural networks and learning systems
Jul 8, 2024
A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracte...
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose local-glob...
IEEE transactions on neural networks and learning systems
Jul 8, 2024
Decoding emotional states from human brain activity play an important role in the brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity patte...
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into ...
IEEE journal of biomedical and health informatics
Jul 2, 2024
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 ...
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...
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...
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...
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...