Artificial Intelligence-Driven Advancements in Non-Invasive Neural Communication: A Deep Learning Approach to Neurolinguistic Learning
Abstract
This study investigates innovative, non-invasive methods for neural communication in the context of language by combining neuroscience, machine learning, and artificial intelligence in the realm of neurolinguistic learning. Our study aims to decipher neural patterns associated with language comprehension using deep recurrent neural networks (RNNs) and gated recurrent units (GRUs). The primary objective is to enhance interactions between neuro-devices and artificial intelligence (AI) through non-intrusive means, thereby improving brain-machine interfaces and neuroprosthetics. The project entails developing an advanced deep RNN-GRU model in Python, utilizing AI to accurately capture intricate neural patterns for language processing. The results indicate the significant potential of non-invasive brain language decoding systems for assistive technologies and brain-machine interfaces. Our AI-enhanced model achieved a notable accuracy rate of 91%, which represents a substantial improvement of 31.4% compared to traditional approaches. This substantial improvement illustrates AI's superior capacity for extracting complex language patterns from non-invasive brain signals, emphasizing its crucial role in advancing neural communication. Moreover, the research underscores the broader implications of integrating AI with neuro-devices, laying the groundwork for future innovations in cognitive enhancement and rehabilitation, and ultimately contributing to the development of more effective and user-friendly assistive technologies.