Abstract
A personal computer may be used, with input devices such as a keyboard,
mouse, and joystick serving as an interface between the computers and the human. The
euphemistic, physically challenged are unable to use these computer systems, therefore,
BCI technology has advanced external applications to be managed without physical
movements in order to assist these physically disabled people and address the
limitations of HCI. The technological advancement in the field of cognitive
neuroscience and brain imaging has enabled it to communicate directly with the human
brain instead of using an interface. Rather than generating signals from muscle
movements, these systems use brain activity to monitor computers or communication
devices. Researchers in the field of Human-Computer Interaction (HCI) look at ways
for machines to utilize as many sensory sources as possible. Furthermore, researchers
have begun to consider implicit types of data, input that is not specifically performed to
instruct a machine to perform a task. Systems can evolve dynamically based on this
data in order to assist the user with the task at hand. Here we discussed components of
Brain-Computer Interface, its characteristics and challenges. The researchers are
attempting to replace conventional classifiers with Convolutional neural networks
(CNNs) that would provide a promising advantage in classification. The EEG signals
from the brain can be linked seamlessly to mechanical systems via BCI applications,
making it a rapidly growing technology that has applications in fields such as Artificial
Intelligence and Computational Intelligence.