AbstractBrain-computer interfaces (BCI) use neuroelectric and metabolic brain activity to activate peripheral devices and computers without mediation of the motor system. In order to activate the BCI patients have to learn a certain amount of brain control. Self-regulation of brain activity was found to follow the principles of skill learning and instrumental conditioning. This review focuses on the clinical application of brain-computer interfaces in paralyzed patients with locked-in syndrome and completely locked-in syndrome (CLIS). It was shown that electroencephalogram (EEG)-based brain-computer interfaces allow selection of letters and words in a computer menu with different types of EEG signals. However, in patients with CLIS without any muscular control, particularly of eye movements, classical EEG-based brain-computer interfaces were not successful. Even after implantation of electrodes in the human brain, CLIS patients were unable to communicate. We developed a theoretical model explaining this fundamental deficit in instrumental learning of brain control and voluntary communication: patients in complete paralysis extinguish goal-directed responseoriented thinking and intentions. Therefore, a reflexive classical conditioning procedure was developed and metabolic brain signals measured with near infrared spectroscopy were used in CLIS patients to answer simple questions with a “yes” or “no”-brain response. The data collected so far are promising and show that for the first time CLIS patients communicate with such a BCI system using metabolic brain signals and simple reflexive learning tasks. Finally, brain machine interfaces and rehabilitation in chronic stroke are described demonstrating in chronic stroke patients without any residual upper limb movement a surprising recovery of motor function on the motor level as well as on the brain level. After extensive combined BCI training with behaviorally oriented physiotherapy, significant improvement in motor function was shown in this previously intractable paralysis. In conclusion, clinical application of brain machine interfaces in well-defined and circumscribed neurological disorders have demonstrated surprisingly positive effects. The application of BCIs to psychiatric and clinical-psychological problems, however, at present did not result in substantial improvement of complex behavioral disorders.
Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond
