Title : Machine learning and brain computer interfaces
Abstract:
Machine learning and Brain–Computer Interface (BCI) technologies are converging to create transformative possibilities in neuroscience, medicine, and human–machine interaction. BCIs enable direct communication between the brain and external devices by decoding neural signals, while machine learning provides the computational foundation for interpreting these complex data patterns. This presentation explores recent advances in machine learning methods—particularly deep learning and reinforcement learning—that have dramatically improved the accuracy, adaptability, and real-time performance of BCIs. Applications range from neuroprosthetic control and rehabilitation for individuals with motor impairments to cognitive enhancement, emotion recognition, and adaptive neurofeedback systems. The session also examines challenges such as data scarcity, signal variability, model interpretability, and ethical considerations surrounding privacy and autonomy. By integrating insights from neuroscience, artificial intelligence, and systems engineering, this talk aims to outline both the current state and future directions of intelligent BCIs, emphasizing how machine learning is bridging the gap between thought and action.
