PRESIDENTIAL ADDRESS, 2005 Breaking the silence: Brain–computer interfaces (BCI) for communication and motor control NIELS BIRBAUMER a,b a Institute of Medical Psychology and Behavioral Neurobiology, University of Tu¨ bingen, Tu¨ bingen, Germany b National Institutes of Health, National Institute of Neurological Disorders and Stroke, Human Cortical Physiology, Bethesda, Maryland, USA Abstract Brain–computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force vari- ations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children. Descriptors: Brain–computer interface, Brain–machine interface, EEG, Invasive brain measures, Locked-in syndrome A brain–computer interface (BCI) or brain–machine interface (BMI) activates electronic or mechanical devices with brain ac- tivity alone. BCIs and BMIs allow direct brain communication in completely paralyzed patients and restoration of movement in paralyzed limbs through the transmission of brain signals to the muscles or to external prosthetic devices. We differentiate inva- sive from noninvasive BCIs: Invasive BCIs use activity recorded by brain implanted micro- or macroelectrodes, whereas non- invasive BCIs use brain signals recorded with sensors outside the body boundaries. The brain signals employed for invasive BCIs to date include (1) action potentials from nerve cells or nerve fibers (Kennedy & Adams, 2003; Kennedy, Bakay, Moore, Adams, & Goldwaithe, 2000), (2) synaptic and extracellular field potentials (Nicolelis, 2001; Serruya, Hatsopoulos, Paninski, Fellows, & Donoghue, 2002), and (3) electrocorticograms (ECoG; Lal et al., 2005; Leuthardt, Schalk, Wolpaw, Ojemann, & Moran, 2004). The noninvasive BCIs used (1) slow cortical potentials (SCP) of the EEG (Birbaumer et al., 1999), (2) EEG and MEG oscillations, mainly sensorimotor rhythm (SMR), also called mu-rhythm (Pfurtscheller, Neuper, & Birbaumer, 2005; Pfurtscheller, Neu- per, et al., 2003; Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002), (3) P300 and other event-related brain po- tentials (ERPs; Farwell & Donchin, 1988), (4) BOLD response in functional magnetic resonance imaging (fMRI; Hinterberger et al., 2004; Weiskopf et al., 2003; Weiskopf, Scharnowski, et al., 2005), and (5) near-infrared spectroscopy (NIRS) measuring cortical blood flow (Coyle, Ward, Markham, McDarby, 2004; Sitaram et al., in press). This article reviews the research concerned with invasive and noninvasive BCIs from the perspective of their clinical usefulness for communication and motor restauration in paralysis. The re- views available on invasive BCI in animals (Nicolelis, 2003; Nicolelis, Birbaumer & Mueller, 2004; Schwartz, Taylor, & Tillery, 2001) describe primarily the performance of single neuronal unit resonse patterns for the reconstruction of movement se- quences in healthy animals; if they discuss clinical applications in The author and his work are supported by the Deutsche Forschungs- gemeinschaft (DFG) and the National Institutes of Health (NIH). The editor, Bob Simons, made invaluable suggestions and corrections at all stages of the manuscript’s preparation. The comments of two anonymous reviewers, of Many Donchin, Andrea Ku¨ bler, Theresa Vaughan, and Jon Wolpaw are greatly appreciated. The data from my laboratory presented here could not have been realized without a functioning interdisciplinary research team: the names of the team members appear in the references cited. They deserve all the credit for this work. The manuscript was prepared during my stay as a research fellow at the NIH, NINDS, Bethesda, MD: My friend Leonardo Cohen, M.D., Chief of the Human Cortical Physiology Section at NINDS, and Cornelia Weber made the time in Washington, DC, a unique and productive experience. Address reprint requests to: Niels Birbaumer, Ph.D., Institute of Medical Psychology and Behavioral Neurobiology, MEG-Center, Uni- versity of Tu¨bingen, Gartenstrasse 29, D-72074 Tu¨ bingen, Germany. E-mail: [email protected]. Psychophysiology, 43 (2006), 517–532. Blackwell Publishing Inc. Printed in the USA. Copyright r 2006 Society for Psychophysiological Research DOI: 10.1111/j.1469-8986.2006.00456.x 517
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PRESIDENTIAL ADDRESS, 2005
Breaking the silence: Brain–computer interfaces (BCI)
for communication and motor control
NIELS BIRBAUMERa,b
aInstitute of Medical Psychology and Behavioral Neurobiology, University of Tubingen, Tubingen, GermanybNational Institutes of Health, National Institute of Neurological Disorders and Stroke, Human Cortical Physiology, Bethesda, Maryland, USA
Abstract
Brain–computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone.
Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and
noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved
with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in
syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical
reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs
allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At
present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made.
Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force vari-
ations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs,
like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.
Tillery, 2001) describe primarily the performance of single neuronal
unit resonse patterns for the reconstruction of movement se-
quences in healthy animals; if they discuss clinical applications in
The author and his work are supported by the Deutsche Forschungs-
gemeinschaft (DFG) and the National Institutes of Health (NIH). The
editor, Bob Simons, made invaluable suggestions and corrections at all
stages of themanuscript’s preparation. The comments of two anonymous
reviewers, ofManyDonchin, AndreaKubler, Theresa Vaughan, and Jon
Wolpaw are greatly appreciated. The data frommy laboratory presented
here could not have been realized without a functioning interdisciplinary
research team: the names of the team members appear in the references
cited. They deserve all the credit for this work. The manuscript was
prepared during my stay as a research fellow at the NIH, NINDS,
Bethesda, MD: My friend Leonardo Cohen, M.D., Chief of the Human
Cortical Physiology Section at NINDS, and Cornelia Weber made the
time in Washington, DC, a unique and productive experience.Address reprint requests to: Niels Birbaumer, Ph.D., Institute of
Medical Psychology and Behavioral Neurobiology, MEG-Center, Uni-versity of Tubingen, Gartenstrasse 29, D-72074 Tubingen, Germany.E-mail: [email protected].
Psychophysiology, 43 (2006), 517–532. Blackwell Publishing Inc. Printed in the USA.Copyright r 2006 Society for Psychophysiological ResearchDOI: 10.1111/j.1469-8986.2006.00456.x
517
human patients at all, a science fiction perspective of whatmay be
possible is given without reference to the few published clinical
applications. The noninvasive BCI literature overviews (Kubler,
et al., 2001; Taylor et al., 2002). The plasticity of the cortical
circuits allows learned control of movements directly from the
cellular activity even outside the primary or secondary hom-
uncular representations of the motor cortex (Taylor et al., 2002).
A multielectrode array recording spike and field potentials
simultaneously was implanted in a single quadriplegic patient’s
526 N. Birbaumer
Figure 5. Depression and quality of life in ALS. A: Depressionmeasured with amodified version of the Beck Depression Inventory
in healthy controls, ALS patients at different stages of their disease, and psychiatrically depressed patients. ALS patients are
significantly more depressed than normals but within the normal range. B: Quality of life in different dimensions of daily living for
ALS patients (white bars) and their significant others (green, usually family members). (From Kubler, Nijboer, et al., 2005.)
motor hand area (2004) by Donoghue’s group (personal com-
munication, April 2005). Within a few training sessions, the pa-
tient learned to use neuronal activity from field potentials to
move a computer cursor in several directions comparable to the
tasks used for multidimensional cursor movements in the non-
invasive SMR-BCI reported by Wolpaw and McFarland (2004).
None of the invasive procedures allowed restoration of skillful
movement in paralyzed animals or people in everyday-life situ-
ations. The animals studied in BMI research (Nicolelis, 2003)
were all intact animals who learned to move an artificial device
or curser for food reward without moving their intact arm
in highly artificial laboratory situations. Any generalization
from the invasive animal BCI approach to paralyzed people is
premature.
In contrast to the invasive approaches, SMR-controlled BCIs
developed by Pfurtscheller and colleagues (Pfurtscheller, Neu-
per, et al., 2003; Pfurtscheller et al., 2005) allowed control of
reaching and grasping in high spinal cord lesioned patients. Pfu-
rtscheller, who was the first in testing and implementing SMR-
based BCIs for motor paralysis, demonstrated convincingly the
potential usefulness of noninvasive BCIs for motor restoration,
more clearly than the widely acclaimed and cited animal experi-
ments using implanted microelectrodes. In one preparation,
Pfurtscheller, Neuper, et al. (2003) used the SMR signals to ac-
tivate electric stimulation electrodes attached to the paralyzed
arm and hand muscles in order to reach and grasp objects in a
quadriplegic patient. These data suggest that, with intelligent
prosthetic devices and orthoses, electrical muscle stimulation,
and EMG feedback from the target muscles, noninvasive BCIs
may have promise for highly complex movement reconstruction.
Neuper, Muller, Kubler, Birbaumer, and Pfurtscheller (2003)
demonstrated successfully that the same SMR-based BCI used
for motor control can be used as a communication device in a
paralyzed cerebral palsy patient and that training and measure-
ment may be performed even from laboratories located at long
distances from the patient. However, none of the paralyzed pa-
tients reported in the literature is using the motor BCI in every-
day-life situations as long as voluntary upper face and shoulder
movements can activate an artificial limb. Therefore, in spinal
cord lesioned patients, invasive and noninvasive BCIs (BMIs)
may be useful in the future for the few patients with extremely
high spinal cord lesions only.
Another obstacle for real-life daily use of BCIs regardless of
the type of application is their demand on attention. Whereas
simple motor commands in the intact adult organism are exe-
cuted with a minimum of cognitive resource allocation, the vol-
untary production of brain signals irrespective of the type of
signal needs more and continuous attentional resource mobil-
ization than highly automatized skills because automatization of
brain control is slow and probably never complete (Neumann
et al., 2004). In addition, the noninvasive BCIs allow relatively
undisturbed slowverbal communication, but production ofmove-
ment with brain activity inevitably generates movement-related
artifacts difficult to eliminate online. Particularly in patients with
spasticity and uncontrolled movement episodes, attempts to
produce motor action from EEG signals are often punished by
the presence of these artifacts and cause frustration and decline in
motivation (Birbaumer et al., 2003, 2004; Kubler, Winter, &
Birbaumer, 2003). For these special cases, the implantation of
electrodes may constitute a viable alternative. Whether the elec-
trodes need to penetrate hundreds to thousands of neurons as
some maintain (Nicolelis, 2003) or only small samples of
Breaking the silence 527
Figure 6. Local brain activation measured with fMRI to 60 affective slides with social content. A: Twelve patients with ALS and 14
age-matched healthy controls at two time points. B: Same group after 6 months of disease progression. Activations of healthy
controls subtracted from ALS. Activations in yellow-red indicate more activation in ALS (Lule et al., in press).
critically important neurons responsible for directional tuning,
for example, is an unresolved question.
Birbaumer, Weber, Buch, Neuper and Cohen (in press) at the
National Institute of Neurological Diseases and Stroke
(NINDS) together with the Tubingen group (Lal et al., 2006)
developed a BCI system for chronic stroke that may solve most
of the problems of noninvasive BCIs devoted to motor restor-
ation and may constitute a sensitive alternative to invasive ap-
proaches. In this preparation, patients with no residual hand
movement are trained with a magnetoencophalography (MEG)-
contolled hand orthosis (Figure 7).
For the first 10 to 20 training sessions in the MEG and after
successful hand opening, closing, and grasping using feedback
and modulation of central SMR magnetic-field oscillations, the
patient is switched to a mobile EEG-SMR-based BCI wearing
the same orthosis. Because brain magnetic fields are not attenu-
ated and distorted on their way from the cortical generators to
the MEG dewar containing the recording SQUIDs, MEG pro-
vides a much larger and more localized SMR response, allowing
control of even single fingers (Braun, Schweizer, Elbert, Birbau-
mer, & Taub, 2000). The head of the patient is fixated in the
dewar and the fingers attached to the orthosis open and close the
hand contingent on SMR increase and decrease. The patient
receives visual and proprioceptive feedback from his/her own
movement and simultaneously watches a screen with an up or
down moving cursor that indicates the amount of SMR present
in the appropriate cortical region 7 s before the self-produced
SMR moves the orthosis attached to the hand. Figure 8 depicts
the SMRmagnetic field localization and training performance of
a patient with long-standing chronic stroke and complete im-
mobility of the affected hand. As a positive side effect, the patient
experienced complete relief of hand spasticity after several train-
ing sessions.
The primary aim of the MEG-BCI training in chronic stroke
is not only restoration of movement but cortical reorganization
and compensatory cerebral activation of nonlesioned brain re-
528 N. Birbaumer
Figure 7. BCI using sensorimotormagnetic field oscillations (CTFMEG
275 channels) for motor restoration of paralyzed hand in chronic stroke.
Top: Feedback curser at the screen indicates amount of SMR present
during 7 s; the goal at the right side of the screen indicates whether the
patient has to increase SMR (lower goal) or decrease it (upper goal). The
orthosis moves the hand proportional to the SMR changed achieved.
Bottom: Experimental situation inMEGwith fingers fixed to the orthosis
opening and closing the hand.
Figure 8. Magnetic field SMR-BCI in a chronic stroke patient. Top:
Magnetic field distribution of 9 Hz magnetic SMR (yellow-brown)
parietal, posterior of lesion, ipsilesional. Bottom: Learning of SMR
control in a chronic stroke patient over 11 sessions.
gions through voluntary brain-controlled hand movement of the
paralyzed limb and reduction of contralesional hemispheric in-
hibition. Duque et al. (2005), Murase, Duque, Mazzocchio, and
Cohen (2004), and Ward and Cohen (2004) have shown in a
series of transcranial magnetic stimulation (TMS) experiments
that the strong inhibitory effect from the healthy hemisphere on
the lesioned hemisphere may be responsible for the lack of re-
organization and insufficient recovery of the stroke-affected
brain area. Consequently, the MEG-BCI training is targeted to-
ward a ‘‘strenghthening’’ of the ipsilesional brain regions around
the destroyed tissue and ‘‘weakening’’ of the homotypical regions
in the opposite hemisphere. This is achieved by using SMR os-
cillations (from 10 to 20 Hz) as a movement-directing source
originating in the immediate neighborhood of the lesion and
simultaneous interruption of feedback and orthosis control with
contralesional coactivation. Cortical reorganization is measured
before and after training with fMRI of imagined and executed
hand and lip movements as described by Lotze et al. (Lotze,
Braun, Birbaumer, Anders, & Cohen, 2003; Lotze, Grodd, et al.,
1999; Lotze, Montoya, et al., 1999). Whether the training results
in improved hand mobility with or without orthosis is the ques-
tion of the ongoing clinical experiments. Chronic stroke with no
remaining finger mobility is resistant to treatment and shows no
spontaneous recovery; any improvement through BCI training
therefore constitutes a success. Again, invasive implantation of
large quantities of electrodes with the many risks and uncertain-
ties involved may be superfluous or reserved for the few most
difficult cases.
Future Directions: The Metabolic Whole Brain BCI
Weiskopf et al. (2003) for the first time demonstrated convin-
cingly that healthy persons are able to regulate BOLD (blood
oxygen level dependent) responses from circumscribed cortical
and subcortical brain regions using online functional magnetic
resonance imaging (fMRI-BCI). These authors and others
(DeCharms et al., 2005) demonstrated substantial effects of
BOLD-response BCI training on behavior: Pain, emotional
arousal, and memory were investigated and astonishingly strong
effects on the behavioral variables after short training periods
with fMRI-feedback training were shown. This is not surprising,
considering that vascular changes in brain arteries and veins
responsible for metabolic responses such as BOLD and brain
blood flow may allow superior voluntary (operant) control be-
cause of the vascular-motor component of the physiological
target response. Dilation and contraction of vascular changes are
sensed by the brain and regulated by neural structures with
closely coupled autonomic and somatic-motor functions,
allowing access to voluntary control (Dworkin, 1993).
The results presented by Weiskopf et al. (2004), Weiskopf,
Klose, Birbaumer, and Mathiak (2005), and Weiskopf, Schar-
nowski, et al. (2005) constitute the first step in the application of
fMRI-BCI to emotional disorders: fMRI allows anatomically
specific control of subcortical and cortical areas responsible
for the regulation of emotions not as accessible to electro-
physiological methods as EEG and MEG such as amygdala,
limbic insular and cingulate regions, and anterior basal ganglia
(Figure 9).
Clinical application of fMRI-BCI is presently unrealistic and
unlikely, considering the cost and technological difficulties in-
volved in real-time fMRI. It will, at present, remain reserved for
research purposes and experiments intending to demonstrate ef-
fects of learned local blood-flow changes on emotional and mo-
tivational behavior. A clinically more realistic new metabolic
BCI system has been proposed and tested recently by Sitaram
et al. (in press). These investigators usednear-infrared spectroscopy
(NIRS) andmeasured, with optical recording devices, changes in
cortical oxygenation and deoxygenation. Using the reflection of
light in living tissues with high circulation density such as the
brain, NIRS is completely noninvasive (Coyle et al., 2004).
NIRS devices are also relatively inexpensive (price equivalent to
that of a multichannel EEG) and commercially available. An-
other virtue of NIRS is portability, allowing, for example, the
training of young children. Sitaram et al. (in press) demonstrated
online operant control of sensorimotor brain areas in five healthy
subjects and spelling of letters with NIRS-BCI with an accuracy
of 70%–95% after only two training sessions and with informa-
tion transfer speed comparable to EEG-BCI.
Epilog
Brain–computer interfaces or brain–machine interfaces are in-
tended to translate ‘‘thought into action’’ with brain activity
only. The research devoted to this goal has raised many fascin-
ating questions about brain–behavior relationships without
achieving its ultimate practical goals: communication with the
completely paralyzed and restoration of movement in paralysis.
But the reformulation of the problem of how brain cells and their
output create observable behavior applied to an existential prob-
lem of human suffering will focus the questions we ask in cog-
nitive neuroscience and psychophysiology. BCI research
stimulates long-held hope and expectation of thought and emo-
tion detection and translation from brain states. And true to the
old Yiddish saying, ‘‘Fur lojter hofenung wer ich noch mes-
chugge’’ [I am crazy with hope].
Breaking the silence 529
Figure 9. FMRI-BCI, experimental setup. Subject (brain in center)
watches screen with yellow line (left) representing BOLD response.
Required increase of BOLD is indicated by green bar, decrease by blue
bar. Signals are processed in a 3 T Siemens Trio Scanner (right) online
using Brain Voyager (below right). Below left: Subject receives feedback
of the BOLD difference between two areas of interest (from Weiskopf
et al. 2004; Weiskopf, Veit, et al., 2005).
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