In daily life and communication, people usually recognize the emotions of people around them through facial expressions, voice dialogues, body language and other external behavioral signals, so as to realize natural communication and interaction with emotion. How to make the machine recognize real-time and accurate human emotions like human beings, and on this basis, carry out more natural and friendlier human-computer interactions becomes one of the important goals of emotion artificial Intelligence or affective computing. A brain-computer interface (BCI) is a direct communication pathway between a brain and an external device, the BCI does not only rely on direct measurement of brain activity but also includes signals from other physiological activities such as EOG, EMG, EEG, or ECG. Affective Brain-Computer Interaction (aBCI) uses neurophysiological signals (e.g., electroencephalography (EEG) signal) to extract brain features that are related to affective states (e.g., emotions and moods) and aims to enhance brain-computer interaction systems with the ability to detect, process, and respond to users affect, emotion, or mood. This affective BCI is now being incorporated into the treatment, offering the promise of a greatly enhanced quality of life by developing cognitive prosthetics for many psychiatric diseases such as anxiety.
Affective BCI can be divided into two categories: one is emotion recognition only, which is called emotion recognition BCI; the other is to regulate people's emotions based on emotion recognition, which is called emotion regulation brain-computer interface. At present, most of the research on affective BCI at home and abroad belongs to emotion recognition BCI, while the research on emotion regulation BCI is just in its infancy. Brain activity signal is closely related to emotion and cognition. Signal processing technology and deep learning method are used to analyze EEG signals and extract features related to emotion and cognition, so as to detect people's internal states. Specifically, the process of EEG signal processing and analysis generally includes preprocessing, feature extraction, feature smoothing, training classifier, and testing, etc.
To promote the development of affective brain-computer interface, this Research Topic will focus on various new technologies or methods that could: improve emotion-related brain data analyses, broaden understanding of emotion-related neural mechanisms, explore emotion biomarkers, suggest effective brain signal processing methods, and expand other brain-inspired applications in affective intelligence.
This Research Topic welcomes Original Research and Review on topics including, but not limited to:
• Emotion recognition studies
• Emotion recognition and regulation studies
• Brain-machine interface, Brain-computer interface technologies
• Cognitive neuroscience and technology
• Wearable device and biomedical sensors
• Machine learning algorithms
• Medical image processing
• Applications of various intelligent technologies in affective intelligence
• Application of artificial intelligence for medical engineering
• Deep learning (CNN, RNN, GAN, etc.) and aBCI
This Research Topic has collaborated with the IEEE 16th International Conference on Complex Medical Engineering (http://www.cme2022.org/) and we welcome submissions from the conference participants.
In daily life and communication, people usually recognize the emotions of people around them through facial expressions, voice dialogues, body language and other external behavioral signals, so as to realize natural communication and interaction with emotion. How to make the machine recognize real-time and accurate human emotions like human beings, and on this basis, carry out more natural and friendlier human-computer interactions becomes one of the important goals of emotion artificial Intelligence or affective computing. A brain-computer interface (BCI) is a direct communication pathway between a brain and an external device, the BCI does not only rely on direct measurement of brain activity but also includes signals from other physiological activities such as EOG, EMG, EEG, or ECG. Affective Brain-Computer Interaction (aBCI) uses neurophysiological signals (e.g., electroencephalography (EEG) signal) to extract brain features that are related to affective states (e.g., emotions and moods) and aims to enhance brain-computer interaction systems with the ability to detect, process, and respond to users affect, emotion, or mood. This affective BCI is now being incorporated into the treatment, offering the promise of a greatly enhanced quality of life by developing cognitive prosthetics for many psychiatric diseases such as anxiety.
Affective BCI can be divided into two categories: one is emotion recognition only, which is called emotion recognition BCI; the other is to regulate people's emotions based on emotion recognition, which is called emotion regulation brain-computer interface. At present, most of the research on affective BCI at home and abroad belongs to emotion recognition BCI, while the research on emotion regulation BCI is just in its infancy. Brain activity signal is closely related to emotion and cognition. Signal processing technology and deep learning method are used to analyze EEG signals and extract features related to emotion and cognition, so as to detect people's internal states. Specifically, the process of EEG signal processing and analysis generally includes preprocessing, feature extraction, feature smoothing, training classifier, and testing, etc.
To promote the development of affective brain-computer interface, this Research Topic will focus on various new technologies or methods that could: improve emotion-related brain data analyses, broaden understanding of emotion-related neural mechanisms, explore emotion biomarkers, suggest effective brain signal processing methods, and expand other brain-inspired applications in affective intelligence.
This Research Topic welcomes Original Research and Review on topics including, but not limited to:
• Emotion recognition studies
• Emotion recognition and regulation studies
• Brain-machine interface, Brain-computer interface technologies
• Cognitive neuroscience and technology
• Wearable device and biomedical sensors
• Machine learning algorithms
• Medical image processing
• Applications of various intelligent technologies in affective intelligence
• Application of artificial intelligence for medical engineering
• Deep learning (CNN, RNN, GAN, etc.) and aBCI
This Research Topic has collaborated with the IEEE 16th International Conference on Complex Medical Engineering (http://www.cme2022.org/) and we welcome submissions from the conference participants.
