For many years people have speculated that electroencephalographic (EEG) activity or other electrophysiological measures of brain function might provide a new nonmuscular channel for sending messages and commands to the external world ­ a brain-computer interface (BCI). Over the past decade, productive BCI research programs have arisen. Facilitated and encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed or "locked in," with basic communication capabilities so that they can express their wishes to caregivers, operate simple word processing programs, or even control a neuroprosthesis.

A BCI allows a person to communicate with or control the external world without using the brain's normal output pathways of peripheral nerves and muscles. Messages and commands are expressed not by muscle contractions, but rather by electrophysiological signals from the brain. Figure 1 summarizes BCI design and operation. Present-day BCIs determine the wishes of the user from a variety of different signal features. As Table 1 shows, these features include evoked potentials, slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp and cortical neuron activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user have control over the signal features and that the BCI correctly derive the user's intentions from them. BCI operation depends on the interaction of two adaptive controllers, the user, who must maintain close correlation between his or her intentions and these signal features, and the BCI, which must translate them into device commands that accomplish the user's intentions. The user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance.

Present-day BCIs provide maximum information transfer rates up to 25 bits/min. With this relatively limited capacity, they can provide basic communication and control functions (e.g., environmental controls, simple word processing) to those with the severe neuromuscular disabilities, such as those locked in by late-stage amyotrophic lateral sclerosis (ALS) or brainstem stroke. They might also support basic control of a neuroprosthesis that provides hand grasp to those with mid-level cervical spinal cord injuries. More complex BCI applications useful to a larger population of users depend on achievement of greater speed and accuracy, that is, higher information transfer rates.

Future progress depends on attention to the twelve key issues listed in Table 2. With adequate recognition and effective engagement of these issues, BCI systems could provide an important new communication and control option for those with motor disabilities. They might also give to those without disabilities a supplementary control channel or a control channel useful in special circumstances.

Recent review article:
Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehab Engin 8:164-173, 2000.

Electrophysiological signals reflecting brain activity are acquired from electrodes on the scalp or within the head and processed to produce measurements of specific signal features, such as amplitudes of evoked potentials or EEG rhythms or firing rates of single neurons, that reflect the user's intentions. These features are translated into commands that operate a device, such as a word processor or a neuroprosthesis. Successful operation depends on the interaction of two adaptive controllers, the user and the system. The user must develop and maintain good correlation between his or her intentions and the signal features selected by the BCI; and the BCI must select features that the user can control and must translate those features into device commands correctly and efficiently.

The systems listed are described in peer-reviewed articles that report actual communication and control (i.e., the person uses the BCI to control a device and sees the results as they occur).

Visual Evoked Potentials

Vidal JJ. Real-time detection of brain events in EEG. IEEE Proc., Special Issue on Biological Signal Processing and Analysis 1977; 65:633-64.

Sutter EE. The brain response interface: communication through visually induced electrical brain responses. J Microcomp App 1992; 15:31-45.

Middendorf M, McMillan G, Calhoun G, Jones KS. Brain-computer interfaces based on steady-state visual evoked response. IEEE Trans Rehab Eng 2000; 8:211-213.

P300 Evoked Potentials

Farwell LA, Donchin E. Talking off the top your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephal clin Neurophysiol 1998; 70:510-523.

Donchin E, Spencer KM, Wijesinghe R. The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehab Eng 2000; 8:174-179.

Slow Cortical Potentials

Elbert T, Rockstroh B, Lutzenberger W, Birbaumer N. Biofeedback of slow cortical potentials. Electroenceph clin Neurophysiol 1980; 48:293-301.

Birbaumer N, A spelling device for the paralysed. Nature 1999; 398:297-298.

Birbaumer N, Kübler A, Ghanayim N, Hinterberger T, Perelmouter J, Kaiser J, Iversen I, Kochoubey B, Neumann N, Flor H. The thought translation device (TTD) for completely paralyzed patients. IEEE Trans Rehab Eng 2000; 8:190-192.

Sensorimotor Cortex Rhythms and Related Activity

Wolpaw JR, McFarland DJ, Neat GW, Forneris CA. An EEG-based brain-computer interface for cursor control. Electroenceph clin Neurophysiol 1991; 78:252-259.

McFarland DJ, Lefkowicz AT, Wolpaw JR. Design and operation of an EEG-based brain-computer interface (BCI) with digital signal processing technology. Behav Res Meth Instrum Comput 1997; 29:337-345.

Wolpaw JR, McFarland DJ, Vaughan TM. Brain-computer interface research at the Wadsworth Center. IEEE Trans Rehab Eng. 2000; 8:222-225.

Pfurtscheller G, Flotzinger D, Kalcher J. Brain-computer interface - a new communication device for handicapped persons. J Microcomp App 1993; 16:293-9.

Guger C. Schlogl A. Walterspacher D. Pfurtscheller G. Design of an EEG-based brain-computer interface (BCI) from standard components running in real-time under Windows. Biomedizinische Technik 1999; 44:12-16.

Pfurtscheller G, Neuper N, Guger C, Harkam W, Ramoser H, Schlögl A, Obermaier B, Pregenzer M. Current trends in Graz brain-computer interface. IEEE Trans Rehab Eng 2000; 8:216-218.

Kostov A and Polak M. Parallel man-machine training in development of EEG-based cursor control. IEEE Trans Rehab Eng 2000; 8:203-204.

Penny WD, Roberts SJ, Curran EA, Stokes MJ. EEG-based communication: a pattern recognition approach. IEEE Trans Rehab Eng 2000; 8:214-215.

Activity of Cortical Neurons

Kennedy PR and Bakay RA. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 1998; 9:1707-1711.

Kennedy PR, Bakay RAE, Moore MM, Adams K, Goldwaithe J. Direct control of a computer from the human central nervous system. IEEE Trans Rehab Eng 2000; 8:198-202.

• Recognition that BCI research and development is an interdisciplinary endeavor, involving neuroscience, psychology, engineering, mathematics, computer science, and clinical rehabilitation
  
  
• Identification of those signal features, whether evoked potentials, spontaneous rhythms, or single-neuron firing rates, that users are best able to control
  
  
• The extent to which feature control can be independent of neuromuscular control
  
  
• The extent to which feature control depends on normal internal CNS structure and function
  
  
• Development of behavioral methods for helping users to gain and maintain feature control
  
  
• Development of signal acquisition and processing methods for deriving the features
  
  
• Selection of the best algorithms for translating the features into device commands
  
  
• Identification and elimination of artifacts such as EMG and EOG activity
  
  
• Adoption of standard methods for evaluating short-term and long-term BCI performance
  
  
• Selection of appropriate applications and appropriate matching of applications and users
  
  
• Attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those applications that are most important to the user
  
  
• Emphasis on peer-reviewed research publications and conservative responses to media attention