Advances in Neural Network Technologies and Brain-Computer Interfaces

The integration of Neural Network Technologies (NNTs) and Brain-Computer Interfaces (BCIs) has transformed human-machine interaction, enabling direct brain-device communication. BCIs empower individuals with neurological disorders, while neural networks enhance signal processing, pattern recognition, and adaptive learning. Advances in deep learning, including CNNs, RNNs, GANs, and Federated Learning, have improved BCI accuracy in motor imagery, authentication, and neurofeedback. Hybrid BCIs combining multiple neural signals enhance adaptability and real-world application. However, challenges remain in ethics, data privacy, hardware, and personalized AI. Future AI-powered BCIs, brain-to-brain communication, and non-invasive neurostimulation promise breakthroughs in cognition, rehabilitation, and augmentation. This chapter explores key principles, innovations, applications, challenges, and future directions in AI-driven neuroscience.