Brain-Computer Interfaces in 2008: Traditional Assumptions Meet Emerging Realities

Brain-computer interface (BCI) research and development seeks to
produce new augmentative communication and control technology for
people with severe neuromuscular disorders, such as amyotrophic
lateral sclerosis (ALS), brainstem stroke, and high-level spinal cord
injury. The goal is to give these extremely disabled users, who may be
unable to breathe or move their eyes, communication and control
capabilities so that they can express their desires to caregivers or
even operate word processing programs or neuroprostheses. Current BCIs
determine the intent of the user from scalp-recorded electrical brain
signals (EEG), or from electrodes surgically implanted on the cortical
surface (ECoG) or within the brain (neuronal action potentials
(spikes) or local field potentials (LFPs)). These signals are
translated in real time into commands that operate a computer display
or other device. EEG-based BCI systems have just begun to come into
everyday use by people with ALS.

Most BCI research has been based on four assumptions associated
with the common misconception that BCIs read minds. The assumptions
are: (1) that intended actions are fully represented in a few specific
cortical areas and are accurately reflected in signals from those
areas; (2) that the signals representing a given intention are always
the same; (3) that higher resolution recording methods, which detect
these signals in more detail, will give better BCI performance; and
therefore (4) that the central goal of BCI research is to obtain the
highest resolution signals (i.e., neuronal activity) and to optimize
their decoding into action.

In reality, none of these assumptions is consistent with existing
and emerging knowledge. In reality: (1) intended actions result from
the cooperation of many central nervous system (CNS) areas from cortex
to spinal cord: (2) the contributions of these many areas and the
signals from them vary across trials and change continually as the CNS
strives to optimize performance; (3) low-resolution signals may give
BCI performance that equals or exceeds that of high-resolution signals
(compare the videos at the first two websites listed below); and
therefore (4) the central goal of BCI research is to find the signals
that the BCI user can best control, to maximize that control, and to
optimize the translation of that control into action.

Thus, BCIs do not read minds but, rather, allow the user to
develop new skills. Unlike normal motor skills, these new BCI skills
are executed by brain signals rather than muscles. Nevertheless, like
normal motor skills, their acquisition and maintenance depend on the
continual interactions of the CNS with the outcomes produced by its
signals and they reflect the activity-dependent adaptive plasticity
that these interactions induce in the CNS.

This reality implies that BCI research should explore the full
range of available brain signals to find those signals that people can
best control, and should focus on developing signal analysis methods
and user training protocols that facilitate and increase that
control. The high performance variability of current methods (whether
they use high or low resolution signals) is emerging as perhaps the
single most difficult problem. Its solution is particularly important
for the realization and dissemination of BCI systems of significant
practical value for those with severe motor disabilities.

In sum, BCI research and development is coming of age. The
assumptions that dominated its infancy came from other fields and
served other purposes, and these assumptions are now dropping away as
the field confronts and engages its own key issues. The results of
this crucial process will largely determine the ultimate scientific
significance and practical success of this exciting new field.

Web Sites

http://www.bciresearch.org/html/2d_control_8tn.html
http://www.nature.com/nature/journal/v442/n7099/suppinfo/nature04970.html (video 1)
http://www.bci2000.org/
http://www.nibib.nih.gov/HealthEdu/eAdvances/28Nov06
http://www.braincommunication.org

Selected Articles

Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer
interfaces for communication and control. (Invited review) Clin
Neurophysiol 113:767-791, 2002.

Wolpaw JR, McFarland DJ. Control of a two-dimensional movement
signal by a non-invasive brain-computer interface in humans. PNAS
101:17849-17854, 2004.

Leuthardt EC, Schalk G, Wolpaw JR, et al. A brain-computer
interface using electrocorticographic signals in humans. J Neural Eng
1:63-71, 2004.

Schalk G, McFarland DJ, Hinterberger T, et al. BCI2000: A
general-purpose brain-computer interface (BCI) system. IEEE Trans
Biomed Eng 51:1034-1043, 2004.

Kübler A, Nijboer F, Mellinger J, et
al. Patients with ALS can use sensorimotor rhythms to operate a
brain-computer interface. Neurol 64:1775-1277, 2005.

Vaughan TM, McFarland DJ, Schalk G, et al. The Wadsworth BCI
research and development program: at home with BCI. IEEE Trans Neural
Syst Rehab Eng 14:229-233, 2006.

Wolpaw JR, Birbaumer N. Brain-computer interfaces for communication
and control. In: Textbook of Neural Repair and Rehabilitation; Neural
Repair and Plasticity. ME Selzer ME, S Clarke, LG Cohen, P Duncan, FH
Gage (Eds). Cambridge University Press, Cambridge, 2006, pp
602-614.

Wolpaw JR. Brain-computer interfaces as new brain output
pathways. J Physiol 579:613-619, 2006.

Schalk G, Miller KJ, Anderson NR, Wilson JA, Smyth MD, Ojemann JG,
Moran DW, Wolpaw JR, Leuthardt EC. Two-dimensional movement control
using electrocorticographic signals in humans. J Neural Eng 5:75-84,
2008.

McFarland DJ, Krusienski DJ, Sarnacki WA, Wolpaw JR. Emulation
of computer mouse control with a noninvasive brain-computer interface
J Neural Eng 5:101-110, 2008.

Wolpaw JR. Brain-Computer Interface. In: Encyclopedia of
Neuroscience. L Squire, T Albright, F Bloom, F Gage, N Spitzer (Eds).
Academic Press, Oxford, in press.

Support

BCI research at the Wadsworth Center receives or has received
support from the National Institutes of Health (NIBIB, NICHD, NINDS),
the James S. McDonnell Foundation, the ALS Hope Foundation, The NEC
Foundation, and the Altran Foundation.

Biography

Dr. Jonathan R. Wolpaw is Chief of the Laboratory of Nervous System
Disorders at the Wadsworth Center, New York State Dept Health, Albany,
NY, where he has been since 1980. He is a Professor of Biomedical
Sciences, State University of New York at Albany, and is also on the
faculty of Albany Medical College and Ohio State University.

A board-certified neurologist elected to the American Neurological
Association in 1987, Dr. Wolpaw received his postgraduate training at
Mount Sinai Hospital in Cleveland and the University of Vermont, and
his research training at NINCDS and NIMH.

One of his two main research interests is vertebrate learning, as
exemplified by the spinal stretch reflex and its electrical analog the
H-reflex. A major aim in this work is a new therapeutic approach to
spasticity and other forms of abnormal reflex function.

His other main interest is development of
brain-computer interface (BCI) technology for people who are totally
paralyzed or have other severe movement disorders. His lab has
developed a brain-computer interface (BCI) system that allows severely
paralyzed people (who may not be able to breath or even move their
eyes) to use scalp-recorded EEG activity to send e-mails, move a
robotic arm, and perform other functions. The first generation of the
Wadsworth BCI system is now in daily home use by several people
severely disabled by ALS. Using EEG, his group has achieved
multidimensional movement control superior to that reported for any
other human BCI system, non-invasive or invasive. Their
general-purpose BCI software platform, BCI2000, has been shared with
over 230 research groups throughout the world and is becoming the BCI
industry standard.

His group's recent national and international
honors include: Saatchi and Saatchi World Changing Ideas Award
Finalist (2007); American Paraplegia Society Jayanthi Charitable
Foundation Award (2006); World Technology Network Fellow (2006);
Altran Foundation Innovation Award (2005); Pirelli INTERNETional Award
(2005); and James S. McDonnell Foundation 21st Century Research Award
(2003).