Emerging Trends of Biomedical Signal Processing in Intelligent Emotion Recognition

Dear Colleagues,

The importance of emotions in human interactions, decision making, understanding, attention, and memory is not hidden from anyone. The significance of studying the behavioral, physiological, and psychological manifestations of human emotions with the aim of providing human-like emotional responses to computers has led to the emergence of a new science called "affective computing". The existence of numerous applications for an interface capable of evaluating human emotional states has made automatic emotion recognition an active research field, especially in biomedical signal processing.

This Special Issue of Brain Sciences will focus on emerging biomedical signal processing and advanced machine learning in emotion recognition. Original research studies will be prioritized. However, cutting-edge reviews about the state of the art, current restrictions, and forthcoming directions are welcome.

Authors could address a broad range of topics related to intelligent emotion recognition including but not limited to:

• Application of EEG, ECG, speech, and other biosignals in emotional state detection.
• Signal pre-processing, feature extraction/engineering, feature reduction/selection, and channel selection approaches in affective computing applications.
• Application of Artificial Intelligence (AI) and advanced AI methods in emotion recognition.
• Machine learning methods/advanced machine learning/deep learning algorithms in emotion recognition applications.
• Fusion algorithms (data-, feature-, or decision level) in emotion recognition applications.
• Wearable devices for affective computing applications utilizing biosignals.
• Applications of such a system in affect-related disorders such as stress, anxiety, phobia, pain, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, etc.
• Human–machine interface (HCI) and brain–computer interface (BCI)-based affective computing systems.

Assistive devices based on emotion recognition.

Dr. Ateke Goshvarpour
Guest Editor

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