Brain-Computer Interfaces for stroke rehabilitation: Evaluation of feedback and classification strategies in healthy users

A Brain-Computer Interface (BCI) is a tool for reading and interpreting signals that are derived from the user's brain, for example using Electroencephalography (EEG) to record signals from the user's scalp. Based on these signals, applications and external devices can be controlled. In the last decades a variety of different BCIs for communication and control applications were developed. A quite new and promising idea is to utilize BCIs as a tool for stroke rehabilitation. The BCI detects the intention to move and provides online feedback to the user, who is therefore able to train the correct motor control of the affected parts of the body. The aim of this publication is to optimize current BCI-strategies for stroke rehabilitation. Therefore a new method of providing immersive feedback via a 3-D virtual reality environment is evaluated. The second crucial aspect is to gain higher classification accuracy of the BCI. In the past years, in terms of signal processing huge improvements have already been done. Besides this, the latest development in EEG-hardware allows using a higher number of EEG-electrodes compared to former years. Hence, we also tested the influence of an increased number of EEG-electrodes.