the intentions of the user. In the past 20 years, the field has progressed rapidly from fundamental neuroscientific discovery to initial translational applications. Examples are seen in the seminal discoveries by Georgopoulus and Schwartz that neurons in the motor cortex, when taken as a population, can predict the direction and speed of arm movements in monkeys (Georgopoulos et al., 1982, 1986; Moran and Schwartz, 1999a). In the subsequent decades, these findings were translated to increasing levels of brain-derived control in monkeys and to preliminary human clinical trials (Hochberg et al., 2006; Taylor et al., 2002). Fundamental to the evolution of neuroprosthetic application, this brain-derived control is dependent on the emerging understanding of cortical physiology as it encodes information about intentions. In recent years, an emerging understanding of how the cortex encodes motor and nonmotor intentions, sensory perception, and the role that cortical plasticity plays in device control have led to new insights in brain function and BCI application. These new discoveries stand to further expand the potential of neuroprosthetics in regards to both control capability and patient populations that will be served. In this review, we provide an overview of current BCI modalities and of emerging research on the use of nonmotor areas for BCI applications, and we assess their potential for clinical impact.
BRAIN-COMPUTER INTERFACE: DEFINITION AND ESSENTIAL FEATURES
A BCI is a device that can decode human intent from brain activity alone in order to create an alternate communication channel for people with severe motor impairments. More explicitly, a BCI does not require the “brain’s normal output pathways of peripheral nerves and muscles” to facilitate interaction with one’s environment (Wolpaw et al., 2000, 2002). A real-world example of this would entail a quadriplegic subject controlling a cursor on a screen with signals derived from individual neurons recorded in the primary motor cortex without the need of overt motor activity. It is important to emphasize this point. A true BCI creates a completely new output pathway for the brain.
As a new output pathway, the user must have feedback to improve the performance of how one alters one’s electrophysiological signals. Similar to the development of a new motor skill (e.g., learning to play tennis), there must be continuous alteration of the subject’s neuronal output. The output should be matched against feedback from intended actions such that the subject’s output (swinging the tennis racket or altering a brain signal) can be tuned to optimize performance toward the intended goal (getting the ball over the net or moving a cursor toward a target). Thus, the brain must change its signals to improve performance, but additionally the BCI may also be able to adapt to the changing milieu of the user’s brain to further optimize functioning. This dual adaptation requires a certain level of training and a learning curve, both for the user and for