Unlocking the Potential of Brain-Computer Interfaces for Neurologically Disabled Individuals

Neurologically affected people have mobility, cognitive, communication, seizure, sensory, emotional, and daily life obstacles, leading to isolation, financial strain, medication side effects, stigma, and caregiver stress. Customized care and support help improve their quality of life. These patients had normal brain waves. The article discusses the potential of brainwave or electroencephalogram (EEG) signals to rehabilitate post-neurological affected people and improve their quality of life. The EEG signals are detected and processed utilizing an Arduino microcontroller, which converts them into motion signals. These motion signals can be used in brain-controlled wheelchairs, communication devices, and adaptable computer interfaces as assistive technologies to improve mobility, communication, and quality of life. This article presents a framework for a wheelchair system that utilizes brainwave signals and Internet of Things (IoT) devices. The proposed system aims to empower individ-uals with neurological disabilities to navigate the wheelchair in various directions. This article presents a framework for a wheelchair system that utilizes brainwave signals and Internet of Things (IoT) devices that can navigate the wheelchair in various directions. The research utilizes pretrained brainwave signals, each representing distinct motion patterns determined by brainwave frequencies, from Kaggle. In conjunction with an Arduino microcontroller, an interface is developed that analyzes this data to regulate the wheelchair. The proposed system aims to empower individuals with neurological disabilities, enhancing patient mobility and quality of life. The proposed wheelchair has the potential to impact the healthcare sector significantly.