For individuals with mobility limitations, powered wheelchair systems provide improved functionality, increased access to healthcare, education and social activities. Input devices such as joystick and switches can provide the necessary input required for efficient control of the powered wheelchair. For persons with limited dexterity, or fine control of the fingers, access to mechanical hardware such as buttons and joysticks can be quite difficult and sometimes painful. For individuals with conditions such as Traumatic Brain Injury (TBI), Multiple Sclerosis (MS) or Amyotrophic lateral sclerosis (ALS) voluntary control of limb movement maybe substantially limited or completely absent. Brain Computer Interfaces (BCI) are emerging as a possible method to replace the brains normal output pathways of peripheral nerves and muscles, allowing individuals with paralysis a method of communication and computer control. This study involves the analysis of non-invasive electroencephalograms (EEG) arising from the use of a newly developed Human Machine Interface (HMI) for powered wheelchair control. Using a delayed response task, binary classification of left and right movement intentions were classified with a best classification rate of 81.63% from single trial EEG. Results suggest that this method may be used to enhance control of HMI's for individuals with severe mobility limitations.