The integration of Artificial Intelligence (AI) and Machine Learning (ML) into neuroscience has revolutionized the development and efficacy of Brain-Computer Interface (BCI) technologies, particularly for individuals with paralysis. This paper explores an overview to the BCI technology and the transformative role of AI and ML in enhancing BCI systems, which facilitate communication and control of assistive devices by interpreting neural signals. Traditional methods of signal processing, such as the Fourier Transform, have significant limitations in handling the complexity and variability of brain activity. In contrast, AI and ML techniques provide adaptive algorithms capable of real-time analysis, noise reduction, and automated feature extraction, enabling a more accurate interpretation of user intent. Furthermore, the personalization of BCI systems through machine learning allows for tailored solutions that cater to individual differences in brain signals, enhancing user experience and device responsiveness. This synergy between neuroscience and advanced computational techniques not only improves the quality of life for individuals with paralysis but also paves the way for further innovations in neurotechnology and rehabilitation strategies. The findings highlight the critical need for interdisciplinary collaboration in 2advancing BCI technologies and their potential applications in various neurological conditions.
