Brain-computer interfaces: communication and restoration of movement in paralysis - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2007 Mar 15;579(Pt 3):621-36.
doi: 10.1113/jphysiol.2006.125633. Epub 2007 Jan 18.

Brain-computer interfaces: communication and restoration of movement in paralysis

Affiliations
Review

Brain-computer interfaces: communication and restoration of movement in paralysis

Niels Birbaumer et al. J Physiol. .

Abstract

The review describes the status of brain-computer or brain-machine interface research. We focus on non-invasive brain-computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Three different types of non-invasive brain–computer interfaces (BCI) A, an ALS patient select letters from a letter string (below on screen) with slow cortical potentials (SCPs, top row); the spelled words appear at the upper part of the screen. B, sensorimotor rhythm (SMR) training. Imagery of movement desynchronizes the SMR (lower EEG trace) from sensorimotor cortex (coloured gyri below). Feedback appears as a moving cursor (red rectangles) towards the right lower bar on the screen (i.e. desynchronization) or the (non-visible) upper bar on the screen (synchronization). C, P300 brain–computer interface (BCI). The rows and columns of the letter matrix (right) are randomly illuminated horizontally and vertically. The patient concentrates on the letter he or she wants to select (p). The P300 potentials to the desired letter in comparison to the non-desired letters (green) are depicted on the left in red.
Figure 2
Figure 2
BOLD response during cortical negativity and positivity A, BOLD responses during self-generated cortical negativity (first vertical column of three brain views, red indicates increase in BOLD, green decrease) and cortical positivity (second vertical column of left figure). B, activation sites during successful voluntary positive SCP regulation in anterior basal ganglia top row, and premotor cortex (below). Adapted from Hinterberger et al. (2003) with kind permission of Springer Science and Business Media.
Figure 3
Figure 3
Brain–orthosis interface MEG BCI for chronic stroke. A, patients observe their SMR activity represented by the red curser on a screen (right part) after instructed to increase (top goal bar) or decrease (lower goal bar) SMR. Decrease closes and increase opens the hand (right) in 5 steps depending on SMR 2 s before. B, stroke patient with hand in orthosis in the MEG during training. Note that patients not only receive visual feedback from the feedback screen but also from watching and feeling their own paralysed hand moving.
Figure 4
Figure 4
Learning to move a paralysed hand in chronic stroke with the MEG BCI Performance of two chronic stroke patients with no residual hand movement over 20 sessions (170 runs). The light blue line depicts the online performance, the red line the significant linear trend (see text).
Figure 5
Figure 5
Brain activations during social emotional slide viewing A, only areas with larger BOLD responses in paralysed ALS patients than controls are shown. B, the replication of the experiment 6 months after the first session shown in A when patients progressed toward complete paralysis. (From Luléet al. 2005.)
Figure 6
Figure 6
FMRI BCI system for on-line training of BOLD regulation as described in Weiskopf et al. (2005) See text for explanation.
Figure 7
Figure 7
Voluntary increase of brain activity in the insula A, increase in percentage BOLD during operant feedback training of the left insula averaged over 9 healthy subjects and 3 sessions of 20 min each. B, horizontal section at the insular region; BOLD responses averaged over 9 healthy subjects and three training sessions. C, control group of 3 subjects instructed to use emotional imagery without contingent feedback of the BOLD responses over three sessions.

Similar articles

Cited by

References

    1. Adam G. Visceral Perception. New York: Plenum Press; 1998.
    1. Albert S, Rabkin J, Del Bene M, Tider M, Mitsumoto H. Wish to die in end-stage ALS. Neurology. 2005;65:68–74. - PMC - PubMed
    1. Bandura A. Social learning of moral judgements. J Pers Soc Psychol. 1969;11:275–279. - PubMed
    1. Barber TX, Kamiya J, Miller NE. Biofeedback and Self-Control. Chicago: Aldine Series, Aldine; 1971–78.
    1. Berger H. Ueber das Elektrenkephalogramm des Menschen. Arch Psychiatrie Nervenkrankheiten. 1929;87:527–570.

Publication types