Guest Editor(s)
Dr. Weidong Dang
Email: weidongdang@tju.edu.cn
Affiliation: School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Homepage:
Research Interests: brain-computer interface, network Science, neurological disease detection, time Series analysis, artificial Intelligence
Dr. Ke Wang
Email: walker_wang@tju.edu.cn
Affiliation: School of Electrical and Information Engineering, Tianjin University, Tianjin, China
Homepage:
Research Interests: machine learning, adaptive learning systems, robotics.
Dr. Dongmei Lv
Email: dongmeilv@tiangong.edu.cn
Affiliation: School of Artificial Intelligence, Tiangong University, Tianjin, China
Homepage:
Research Interests: motor imagery, EEG decoding, EEG classification, convolutional neural network
Summary
The rapid development of artificial intelligence, machine learning, and advanced signal processing has significantly accelerated research on electroencephalography (EEG) and brain-computer interfaces (BCIs). As key technologies for understanding neural activity and enabling direct interaction between the brain and external systems, EEG and BCI have attracted growing attention in healthcare, neurorehabilitation, cognitive monitoring, affective computing, and human-computer interaction. At the same time, the increasing complexity, variability, and real-time requirements of neural data analysis have created urgent demands for more robust, adaptive, and intelligent computing methods.
This Special Issue aims to explore recent advances in intelligent computing for EEG and BCI research. We seek original contributions that present innovative theories, computational models, algorithms, and practical systems for neural signal analysis, brain-state decoding, and intelligent interaction. The issue welcomes interdisciplinary studies from computer science, artificial intelligence, signal processing, biomedical engineering, and related fields, with particular emphasis on work that demonstrates methodological novelty, technical rigor, and application value.
To address these challenges and opportunities, this Special Issue focuses on the following topics, including but not limited to:
· Intelligent EEG Signal Analysis and Neural Pattern Recognition
· Brain-Computer Interface Modeling, Decoding, and Intelligent Interaction
· EEG-Based Analysis for Neurological Disorders and Brain Health Assessment
· Intelligent Analysis of Driver Fatigue, Attention, and Cognitive States
· EEG-Based Emotion Recognition and Affective State Analysis
· Intelligent Sleep Stage Analysis and Sleep-Related EEG Applications
Keywords
Electroencephalography (EEG), brain-computer interface (BCI), intelligent computing, brain state analysis, neurological disorder assessment, driver fatigue detection, emotion recognition, sleep analysis, deep learning
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