A Two Dimensional Brain-Computer Interface Associated with Human Natural Motor Control

1.1 Target groups of brain-computer interfaces (BCIs) Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects nerve cells which are responsible for controlling voluntary movement. Primary lateral sclerosis (PLS) is a variant of ALS that affects the corticospinal upper motor neurons, limiting movement. ALS/PLS patients, as well as patients disabled from other degenerative diseases or brain injuries, have difficulty with everyday motor behaviors such as moving, swallowing, and speaking. In the later stages of disease, some patients may completely lose motor function and become totally ‘locked-in’ (Hayashi and Oppenheimer, 2003). Loss of motor function significantly affects patients’ quality of life (QoL) (Mockford et al., 2006; Bromberg, 2008; Williams et al., 2008; Lule et al., 2009) and increases the financial burden for the cost of care (Mutsaarts et al., 2004). One important component of quality of life being addressed repeatedly by patients, specifically as the disease progresses, is the ability to communicate. A brain– computer interface (BCI) or brain–machine interface (BMI), has been proposed as an alternative communication pathway, bypassing the normal cortical-muscular pathway (Joseph, 1985; Kennedy et al., 2000). BCI is a system that provides a neural interface to substitute for the loss of normal neuromuscular outputs by enabling individuals to interact with their environment through brain signals rather than muscles (Wolpaw et al., 2002; Daly and Wolpaw, 2008). Recent years have featured a rapid growth of BCI research and development owing to increased societal interest and appreciation of the serious needs and impressive potential of patients with severe motor disabilities (Birbaumer and Cohen, 2007; Daly and Wolpaw, 2008). The majority of BCI-related publications have studied performance in healthy volunteers and focused on the development of signal processing/computational algorithms to improve BCI performance (Bashashati et al., 2007). Practical BCI clinical applications for the potential patient users, however, are still limited (Birbaumer, 2006a).