Feedback strategies for BCI based stroke rehabilitation: Evaluation of different approaches

Abstract

Brain-computer interfaces (BCIs) allow human communication without using the brain’s normal output pathways. A BCI is a tool that converts signals recorded from the user’s brain into control signals for different applications. Most BCI systems are based on one of the following methods: P300; steady-state visually evoked potentials (SSVEP); and event-related desynchronization (ERD). Electroencephalog-ram (EEG) activity is recorded non-invasively using active or passive electrodes mounted on the human scalp. In recent years, a variety of different BCI applications for communication and control were developed. A promising new idea is to utilize BCI systems as tools for brain rehabilitation. The BCI can detect the user’s movement intention and provide online feedback for rehabilitation sessions. In many cases, stroke patients can re-train their brains to restore effective movement. Previous work has continued to show that higher density electrode systems can reveal subtleties of brain dynamics that are not obvious with only few electrodes. This paper tries to optimize current BCI strategies for stroke rehabilitation by comparing conventional bar feedback (bFD) to immersive 3-D virtual reality feedback (VRFB).