Call For Papers

Pattern recognition plays a crucial role in BCI systems for interpreting brain signals and extracting meaningful information, such as identifying user intentions or emotions. Machine learning techniques are essential for developing robust and adaptive BCI systems capable of recognizing patterns in brain activity and translating them into actionable commands or feedback. BCI technology intersects with various disciplines, including neuroscience, computer science, and engineering, making it an interdisciplinary field where pattern recognition algorithms and methodologies are widely applied.

We seek original and high-quality submissions on, but not limited to, one or more of the following topics:

• Introduction to Brain-Computer Interfaces (BCI) and Machine Learning 

• Machine Learning Techniques for BCI Signal Processing and Feature Extraction 

• Applications of BCI in Neuro-Prosthetics and Rehabilitation 

• Assistive technologies for individuals with disabilities 

• Ethical and Social Implications of BCI Technology 

• Case Studies and Real-World Implementations 

• Collaboration between neuroscience, engineering, and computer science 

• Emerging trends in BCI research 

• Opportunities for interdisciplinary research and innovation 

• Leveraging IoT devices and AI for continuous monitoring and analysis of patient data 

• Ensuring secure communication and data integrity for BCI systems integrated with IoT networks. 

• Developing advanced neural network architectures tailored for processing Healthcare Data

• Employing AI techniques to analyze and predict trends in data collected from IoT devices, including BCI data. 

• Edge Computing in IoT for Healthcare 

• IoT-Enabled Smart Environments for Assisted Living 

• Predictive Analytics for IoT-Driven Healthcare Data 

• Ethical Considerations in AI-BCI-IoT Healthcare Systems