The field of brain-computer interfacing has witnessed tremendous growth over the past decade. Invasive BCIs based on multielectrode arrays have allowed laboratory animals to precisely control the movement of robotic arms. Implants and semi-invasive BCIs have enabled human subjects to quickly acquire control of computer cursors and simple devices. Noninvasive BCIs, particularly those based on EEG, have allowed humans to control cursors in multiple dimensions and issue commands to semi-autonomous robots. Commercially available BCIs such as cochlear implants and deep brain stimulators have helped improve the quality of life of hundreds of hearing-impaired individuals and patients suffering from debilitating neurological diseases.

The achievements of the field thus far are impressive, but many obstacles remain. As pointed out by Gilja, Shenoy, and colleagues (2011), invasive BCIs have yet to achieve the same levels of performance, multidecade robustness, and naturalistic proprioception and somatosensation as able-bodied people. Furthermore, invasive BCIs remain risky for humans and are used only as a last resort in severely disabled patients. The most popular noninvasive BCIs, based on EEG, suffer from a number of problems:

• Electrode placement is cumbersome and setup time is typically long (up to half an hour depending on the number of electrodes).

• Results of training and learning may not be transferable from one day to the next due to shifts in electrode locations, noisy contacts with scalp, etc.

• Low signal-to-noise ratio and on-line adaptation in subjects necessitate the availability of powerful amplifiers as well as efficient machine-learning and signal processing algorithms.

• Signal attenuation and summation between the brain and the scalp, together with sparse sampling of activity, limits the range of useful control signals that can be extracted.