The Long term goal of the Brain-Computer Interfaces for Rehabilitation (BCI-Rehab) Laboratory is to investigate and extract EEG signals for remote motor control, enhancing human decision making and monitoring capabilities and motor rehabilitation.
Brain-Computer Interfaces (BCI): BCIs are driven by Electroencephalogram (EEG) signals recorded from the scalp by an array of electrodes mounted in a cap. EEG analysis and BCI have many promising applications for remote control, enhancing human capabilities, and rehabilitation. The success of BCIs in interpreting human intentions depends on efficient preprocessing and machine learning techniques for both feature detection and classification. Typical error rates are currently around 30%, and dealing with real-time error detection and reduction is one of the challenging research areas.
The novel paradigm that we are developing is based on detecting and decoding Error-related Potentials (ErrP), which are evoked in response to errors or conflicts between expected and actual feedback. Such errors may occur not only due to interface errors, but also due to any unexpected feedback or unexpected features in the environment. Thus the information available in error-related potentials is expected to enhance and improve BCIs, remote control, simulator training, decision making, human performance monitoring, and robot-assisted rehabilitation.