Brain-Computer Interface: State-of-Art, Challenges, and the Future

In this chapter, the basic state-of-art and possible future development of the brain–computer interface (BCI) are analyzed. Progress in brain science and computer technology influenced the development of BCI, opening up new possibilities for the neurorehabilitation of people with disabilities, people who are paralyzed, or with brain injuries such as stroke. BCI allows users to control various devices, especially in the case of people with disabilities, using electroencephalographic (EEG) activity of the scalp, which requires extensive training or individual activity of neurons in the brain, which involves significant risk and limited stability of the EEF signal. The BCI-based electrocorticography (ECoG) allows people with extreme motor dysfunctions to have non-muscular communication and stronger control than in the case of EEG-based BCI. Many BCI-related papers describe the decoding of BCI signals related to whole-body, motor-imaginative, kinematics, which involves various senses. Recent technological progress of wireless recording, deep artificial learning analysis, and real-time time resolution are incorporated into BCI technology, thus enabling its rapid development. In addition, specific models for BCI systems have been applied to develop BCI taxonomy. Speed and accuracy have been shown to be critical to the clinical application of BCI, which includes fast translating brain activity into instructions issued to a computer or other device, allowing the user to interact with the surrounding by only brain action instead of peripheral nerves and muscles. Also described are BCI spellers, P300, or visually evoked steady state potential (SSVEP) and motor imaginary (MI).