Recent developments in neural engineering have introduced Brain Machine Interface (BMI) as a potential alternative and augmentative communication and control tool for patients suffering from neurological disorders. BMI is a communication system that bypasses the conventional motor output pathways of nerves and muscles and directly translates brain signals into commands for controlling an external device such as a wheelchair, computer screen, and even a neuroprosthetic device. A non-invasive BMI system acquires brain signals using an electroencephalograph (EEG) and decodes the neural signatures embedded in the neuronal activities using signal processing and machine learning techniques. BMI-based neurofeedback training and rehabilitation is gaining significant attention recently due to its potential to assist patients in gaining self-regulation over the specific neuromarkers of neurological disorders and building brain plasticity. This talk will provide an introduction to BMI-based neurorehabilitation and assistive technology devices and present some of the BMI-based motor and cognitive neuro-rehabilitation techniques developed by our research team.