Brain-machine interfaces (BMIs) offer promising applications for individuals with various neurological diseases and injuries. The most notable applications focus on the use of BMI systems to operate devices that compensate for communication impairments or lost movements. Additional applications, such as the development of cognitive rehabilitative training programs to restore lost abilities have been relatively unexplored. Such programs offer outstanding opportunities for clinical and home-based rehabilitation programs, increasing opportunities for rehabilitation training without requiring additional rehabilitation therapist time.
We use typical brain imaging modalities such as electroencephalography (EEG) to study the electrical activity of the brain, but also novel modalities such as transcranial Doppler sonography to observe cerebral blood flow in major arteries in the brain. We combine these brain imaging modalities with novel signal processing and machine learning approaches to produce novel applications of BMIs.