Brain Science Advances is a peer-reviewed, international, and interdisciplinary open access journal with ISSN 2096-5958 and CN 10-1534/R, which is published quarterly by Tsinghua University Press and SAGE. Brain Science Advances publishes high-quality original research articles and review articles, focusing on basic neuroscience, clinical neuroscience, and neural engineering.

Brain-Computer Interface (BCI) technology is transforming human–machine interaction by enabling direct communication between neural activity and external systems. Recent advances in signal acquisition, adaptive algorithms, and AI-enhanced decoding have moved BCIs from experimental setups to practical applications in healthcare, neurorehabilitation, and Industry 4.0. Modern BCIs leverage EEG, ECoG, and emerging sensor technologies to capture brain signals with high fidelity. Specifically, deep learning and transformer-based models enhance noise reduction, feature extraction, and real-time intent prediction, enabling systems to adapt dynamically to user states and enabling personalized control of prosthetics, robotic platforms, and immersive environments.

Artificial Intelligence plays a pivotal role in this evolution. Machine learning and cognitive computing frameworks now power closed-loop BCIs that continuously learn from user feedback, improving accuracy and reducing training time. These advanced paradigms extend BCI capabilities beyond motor control to emotion recognition, cognitive workload monitoring, and predictive modeling for neurological disorders. Applications span assistive technologies for paralysis, adaptive neurorehabilitation, and intelligent interfaces for collaborative robotics and surgical automation. Crucially, integration with cyber-physical systems and cloud-based AI infrastructures is accelerating scalability and interoperability. Innovations at the intersection of BCI and Human–AI Interaction are creating interfaces that respond to user preferences, emotional states, and workload, making interaction more intuitive, transparent, and user-centric.

This Special Issue invites contributions addressing these frontiers, ranging from novel signal processing and adaptive algorithms to AI-enhanced BCI applications in healthcare, neuroergonomics, and human augmentation. By fostering interdisciplinary dialogue, we aim to advance the next generation of intelligent, user-centric BCI systems that seamlessly merge human cognition with machine intelligence.

Suggested Article Topics

1. AI-Enhanced Signal Decoding for Brain–Computer Interfaces
2. Adaptive Human–Machine Interaction through Cognitive State Monitoring
3. BCI Applications in Neurorehabilitation and Assistive Robotics
4. Ethical and Regulatory Frameworks for AI-Integrated BCI Systems
5. Multimodal BCI Systems: Combining Neural, Physiological, and Behavioral Data
6. AI-Powered Predictive Modeling for Neurological Disorders

Keywords

Brain–Computer Interface (BCI), Human–Machine Interaction (HMI), Artificial Intelligence (AI), Cognitive State Monitoring, Neurorehabilitation, Adaptive Systems, Deep Learning, Ethical Neurotechnology.

Guest Editors

• Prof. Tzyy-Ping Jung, University of California San Diego, 
• Prof. Siddharth Siddharth, Plaksha University
• Prof. Chun-Ling Lin, National Taipei University of Technology
• Prof. Feng Wan, University of Macau

The final submission deadline:  31 July 2026

SUBMISSION GUIDELINES

Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to: mc03.manuscriptcentral.com/brainsa. Further information on the journal is available at: www.sciopen.com/journal/2096-5958