Abstract

Brain-computer interface (BCI) technology has emerged as a groundbreaking innova-tion with transformative potential in medical devices. BCIs are analyzed for their ability to diag-nose, treat, and manage neurological disorders, such as Parkinson's disease, ALS, and stroke.

The study explores the integration of BCI technology into medical devices and examines the challenges and opportunities regulatory authorities face in overseeing this rapidly evolving field.

The study employs a comprehensive literature review with the help of databases like Google Scholar, and PubMed, analyzing case studies and regulatory requirements.

BCI technology enables direct communication between the human brain and external devices, allowing for the control of computers or prosthetic limbs. Additionally, software tools facilitate the analysis of recorded brain signals, aided by advancements in Artificial Intelligence (AI), in-cluding Machine Learning (ML) and Deep Learning (DL), for automatic classification of EEG signals. However, the rapid advancement leads to high costs and complexity of BCI systems which can limit their accessibility and scalability, posing a barrier. Moreover, the development of standardized protocols and guidelines for BCI implementation is essential to maintain con-sistency and reliability across applications.

The ethical considerations surrounding BCI technology are vital and emphasize the need for government regulations to ensure its safe and effective integration into healthcare. BCI's poten-tial for responsible innovation in patient-centric care is advocated, propelling medical technolo-gy into a new era of seamless integration and improved patient outcomes.

Keywords: Brain-Computer interface, regulation, FDA, electroencephalogram (EEG), signal, innovation