AI AND NEURAL INTERFACES: EMPOWERING COMMUNICATION FOR PHYSICALLY CHALLENGED INDIVIDUALS

Severe physical conditions such as locked-in syndrome, amyotrophic lateral sclerosis (ALS), and post-stroke paralysis can greatly limit a person’s ability to speak or move, even though their thinking and understanding remain unaffected. This mismatch between cognitive ability and physical expression creates major obstacles in communication and everyday independence. This paper investigates how artificial intelligence (AI), when combined with neural interface technologies, can help overcome these limitations and provide more effective means of interaction.

The study focuses on the use of brain–computer interfaces (BCIs) and neuroprosthetic systems that capture and interpret neural signals directly from the brain. AI-based approaches are applied to process these signals and transform them into practical outputs, including text, speech, or control commands for assistive technologies. Adaptive learning models allow the systems to adjust to individual users, leading to improved performance and reliability over continued use.

The findings indicate that AI-supported neural interfaces significantly enhance communication efficiency and usability compared to conventional assistive methods. Beyond communication, these technologies also enable users to operate computers, mobility devices, robotic aids, and smart systems within their environment. Overall, the paper concludes that AI-driven neural interfaces hold considerable promise for improving communication, autonomy, and quality of life for individuals with severe physical impairments.