NEUROLOGICAL NAVIGATION AND COGNITIVE MOBILITY: THE RESEARCH AND DEVELOPMENT OF BRAIN-CONTROLLED WHEELCHAIRS
The goal of brain-computer interface research is to convert neural instructions into control signals automatically. Applications like text input programs, electronic wheelchairs, or neuro prosthetics can then be controlled by them. For patients who are extremely incapacitated, a BCI system can operate as a communication channel. For healthy users, it can function as an extra man-machine interface. The traditional "operant conditioning" method required weeks or months of training for people to retrain their brain signals to use the system. The Berlin Brain-Computer Interface project (BBCI) has created an electroencephalogram (EEG)-based system that uses cutting-edge machine learning techniques to replace operant training. Even participants without prior BCI experience can obtain high information transmission rates from their first session by learning to modify classifiers to the very subject-specific brain signals. Nevertheless, brain signals are seldom sufficiently steady after an initial calibration that the first classifier may be used again in the same experimental session. Wheelchairs with intelligence are a big help to those who need it. Moving can be challenging for those who suffer from movement difficulties brought on by certain illnesses, such as multiple sclerosis or stroke. They therefore require the ongoing assistance of carers. In order to aid people with infirmities and paralysis, this article introduces brain-controlled wheelchairs. By deciphering data from an electroencephalogram (EEG), or brain waves, it manages the wheelchair. When using EEG, the user applies an electrode cap to their scalp to detect EEG signals. The Arduino microcontroller then interprets these data and uses them to generate motor commands that move the wheelchair. Enhance the quality of life for individuals who are paralyzed by using the Mind Controlled Wheelchair.
EEG Signals, BCI, Classifiers, Neural Commands, Control Signals, fMR1, ECOG, Eyebro