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  Brain Machine Interfaces: Mind over Matter  
 

José C. Principe
Computational NeuroEngineering Laboratory,
University of Florida

 

Our brains use our bodies to interact with the external world. However, emerging technology is poised to challenge this state of affairs and create direct brain machine interfaces (BMIs) to open up a digital channel between the brain and the physical world. The seamless integration of brain and body in the healthy human makes us forget that under some circumstances, brains can be deprived of their sensing abilities (for example, blindness or deafness) or motor abilities (for example, paralysis). The general concept is therefore to either create artificial sensory systems by delivering the external world stimuli to the appropriate regions of the brain (as seen in retinal and cochlear implants), or to allow the brain to directly command and control external devices, such as computer cursors or robotic prostheses.

Potentially, BMIs may enable a higher bandwidth interface between the human brain and the digital computer, circumventing the need for computer mice and keyboards, and rewriting our metaphors on how humans interact with computers. BMIs can also augment humans’ natural abilities by providing mind-based control of engineered devices that are much faster and more powerful than biological tissue.

1. Sensory BMIs

BMIs can be divided in two basic types, depending upon the application: sensory or motor. Sensory BMIs stimulate sensory areas of the brain with signals that are generated from external physical stimuli. The most common sensory BMIs, with over 50,000 implants, are cochlear implants that allow deaf people to hear, by translating sounds from wave pressure in the ear into spike firings applied to the auditory nerve. The same basic concept is being developed for a retinal prosthesis, which allows blind people to see outlines of external objects by delivering the appropriate stimulation to the visual cortex.

Motor BMIs, on the other hand, seek to translate electrical brain activity that represents an intent to move into useful commands to external devices; they are the ones emphasized here.

The two types of BMIs are very different. Sensory BMIs require very accurate placement of a few tiny electrodes that stimulate the appropriate site in the brain, and the device’s job is to simulate the role of the appropriate sensory organ as accurately as possible. In motor BMIs, the electrodes are placed “anywhere” in the appropriate cortex area (such as the area that controls the right hand), and their number is much higher. The decoding problem for motor BMIs is much harder, both because there is little knowledge of how the motor cortex encodes information, and because only a small fraction of the cells is being probed.

2. Brain computer interfaces (BCIs)

There are two basic types of motor BMIs: non-invasive and invasive. Research on non-invasive BMIs started in the 1980s by measuring brain electrical activity over the scalp (electroencephalogram (EEG)). Through training, subjects learn to control their brain activity in a predetermined fashion that is classified by a pattern recognition algorithm, and converted into one of several discrete commands -- usually cursor actions (up/down, left/right) on a computer display. The computer presents a set of possibilities to the users, and they choose one of them through these cursor actions, until a task is completed. This approach, requiring only signal amplification and classification, is known as a brain computer interface (BCI).

BCI classification algorithms combine machine learning techniques with biomedical domain knowledge. There is now an annual competition to evaluate the progress of these algorithms [1], where EEG data sets are made publicly available, together with a performance measure. Each data set has a labeled and an unlabeled part; contestants submit estimated labels for the test data, which are then evaluated according to the given performance measure. At present, the best algorithms still have high error rates, as high as 20 percent for some tasks.

BCIs require lengthy subject training through biofeedback, and they display a low bandwidth for effective communication (15 to 25 bits per minute) [2], hindering the speed at which tasks can be accomplished, even with the most accurate classification algorithms. However, BCIs have already been tested with success in paralyzed patients. Several groups all over the world have demonstrated working versions of BCIs [2], and a system software standard has been proposed [3].

3. Control BMIs

In the last decade, emerging developments in microchip design, signal processing algorithms, computers, sensors, and robotics are coalescing into a new technology devoted to creating a different type of BMI, which translates brain activity in a specific area of the motor cortex into the corresponding movement of some device in two-dimensional (2D) or three-dimensional (3D) space – so-called trajectory control BMIs. For example, electrodes placed in the part of the brain that controls the right hand can provide real-time control of cursor movements on a computer screen, as if a mouse is being used. These BMIs can be thought of as intelligent agents that translate intention of movement from the biological “wetware” to the firmware of a robotic actuator.

The technical problem with control BMIs cannot be solved by classifying brain signals into a small set of discrete commands, as in BCIs. Rather, the algorithms must translate spike firings in real time into continuous output that represents motion. While the EEG signals obtained noninvasively do not provide sufficient resolution for trajectory control, implanted electrodes that directly sense the brain’s neuronal firings or local field potentials make it possible. Hence, control BMIs are invasive, probing hundreds of neurons at once.

Figure 1 shows the architecture of a control BMI.

Figure 1: Schematic of a control BMI

4. The future

BMIs are still in their infancy, even sensory BMIs. For instance, the cochlea has more than 100,000 neuronal connections, but with cochlear implants we are implanting just ten channels to tens of neurons! For most of us, this would be awful resolution, but it’s amazing for someone who was incapable of hearing before receiving an implant.

Unlike sensory BMIs, control BMIs are still at the proof-of-concept stage; recently we have seen the first human BMI implants, after successful experiments with primates [4]. So far, this work was driven by neuroscience research, and the role of computer scientists has been modest. But now, to take BMIs to the next level, issues of miniaturization of the electronics and algorithm accuracy and scalability have become crucial. The challenges are many and difficult; we identify four directions of computing research:

The development of more accurate data models that carry more spatio-temporal information from the spikes in the motor cortex. The signals are non-gaussian and nonstationary, so they are very difficult to model well with present algorithms.

The development of algorithmic paradigms that scale up well for a variety of movement tasks. So far, control BMIs have focused on cursor movements, but a mechanical hand has much more freedom of motion than a mouse.

Haptic interfaces in robotics. For successful task completion, it will be necessary to provide feedback to the user (aside from visual feedback), so the user can “feel” the objects being touched by the mechanical hand.

Intelligent system design. Where to place resources for better overall performance is unknown, but the robot will probably have partial autonomy, rather than be wholly subjugated to the user’s control. For instance, if the mechanical hand is close to a glass of water, the robotic interface may have to grab and lift the glass autonomously.

Although the theoretical and technical problems are exceedingly difficult, motor BMI research is at a very exciting phase, thanks to the tight integration of research in computer science, engineering, and neuroscience. There is optimism about impacting the daily lives of paraplegics in the same way that sensory BMIs benefited hearing impaired patients.

There is also hope that, someday, field potentials collected at the scalp will provide sufficient spatio-temporal resolution to construct trajectory control BMIs noninvasively. The convenience of less invasive BMIs would increase their applicability beyond the restoration of lost movement in paraplegics, and would enable normal individuals to have direct brain control of external devices in their daily lives.

The technological explosion through the centuries is proof that human society continuously seeks more sophisticated tools and faster and more powerful forms of communication. BMIs are the enabling medium that allows humans to extend the expression of their intent far beyond what is provided by simple body motion or speech. As mobile communications, personal computing, and the Internet become more integrated into our homes, workplaces, and transportation, it is foreseeable that we will naturally seek out BMIs that enable seamless direct brain control of intelligent agents embedded in our technology devices. Therefore, the impact of BMIs on our society promises to surpass that of any earlier digital technology.

 

Created: Mar 11 2005
Last updated: Mar 11 2005


  "The problem of restoring amputees to function, as well as the problem of restoring to function people who have lost one or more of their senses, [is] interesting ... as a method of exploration [of] ... the nervo-muscular sensory reflex loop… We are very far from doing it in a stable, viable way."
-Norbert Wiener (1894-1964), writing in Introduction to neurocybernetics, 1963.
 
  Web pages

The Computational NeuroEngineering Laboratory (CNEL): a lab at the University of Florida that conducts research on adaptive information processing systems.

The Donoghue Lab: a group at Brown University that looks at how the brain turns thought into action. The scientists at the lab are also working on building prosthetic devices that provide an interface between the brain and the external world for paralyzed people.

Laboratory of Miguel A.L. Nicolelis: a group at Duke University Medical Center that investigates computational principles underlying the dynamics between cortical and subcortical neurons mediating tactile perception.

Articles

IEEE Transactions on Biomedical Engineering 51, 6 (2004). (Special issue on BCIs and BMIs)

Jonietz, E. Picking your brain. Technology Review 107, 9 (2004), 74-75.

Moore, M.M.; Dua, U. A galvanic skin response interface for people with severe motor disabilities. In Proc. of the ACM SIGACCESS Conference on Computers and Accessibility (ASSETS ’04) (Atlanta, GA, Oct. 18-20, 2004), pp. 48-54.

Nicolelis, M.A.L.; Chapin, J.K. Controlling robots with the mind. Scientific American October (2002), 47-53.

Schalk, G.; McFarland, D.; Hinterberger, T.; Birbaumer, N.; Wolpaw, J. BCI2000: a general purpose brain computer interface. IEEE Transactions on Biomedical Engineering 51, 6 (2004), 1034-1043.

Schwartz, A.B.; Taylor, D.M.; Helms Tillery S.I. Extraction algorithms for cortical control of arm prosthetics. Current Opinion in Neurobiology 11, 6 (2001), 701-708.

Wessberg, J.; Stambaugh, C.R.; Kralik, J.D.; Beck, P.D.; Laubach, M.; Chapin, J.K.; Kim, J.; Biggs, S.J.; Srinivasan, M.A.; Nicolelis, M.A.L. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408 (2000), 361-365.

Wolpaw, J.R.; Birbaumer, N.; McFarland, D.J.; Pfurtscheller, G.; Vaughan, T.M. Brain computer interfaces for communication and control. Clinical Neurophysiology 113 (2002), 767-791.

Books

Methods for neural ensemble recordings. Nicolelis M.A.L., 1998.

Neural networks: a comprehensive foundation (2nd ed.). Haykin S., 1998.

Spikes: exploring the neural code. Rieke F., Warland, D., de Ruyter van Steveninck, R., Bialek, W., 1999.

Conferences

The Third BCI Competition: a competition for fostering research interest in advanced signal processing and classification methods.

Neural Information Processing Systems (NIPS) Conference 2004: an annual conference, sponsored by the NIPS Foundation, that focuses on the biological, technological, mathematical, and theoretical aspects of neural information processing systems.

The 2nd International IEEE/EMBS Conference on Neural Engineering: an upcoming conference sponsored by the IEEE Engineering in Medicine and Biology Society (EMBS) that will focus on the neural engineering field, covering such areas as restoring lost sensory and motor abilities, neurorobotics, and neuroelectronics.

Reviews

A galvanic skin response interface for people with severe motor disabilities. Moore M., Dua U. ASSETS '04: 48-54, 2004

Human factors issues in the neural signals direct brain-computer interfaces Moore M., Kennedy P. ASSETS '00: 114-120, 2000

 


1) PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning. BCI Competition III Challenge. http://www.pascal-network.org/Challenges/BCI3/.
2) Wolpaw, J.R.; Birbaumer, N.; McFarland, D.J.; Pfurtscheller, G.; Vaughan, T.M. Brain computer interfaces for communication and control. Clinical Neurophysiology 113 (2002), 767-791.
3) Schalk, G.; MacFarland, D.; Hinterberger, T.; Birbaumer, N.; Wolpaw, J. BCI2000: a general purpose brain computer interface. IEEE Transactions on Biomedical Engineering 51, 6 (2004), 1034-1043.
4) Wessberg, J.; Stambaugh, C.R.; Kralik, J.D.; Beck, P.D.; Laubach, M.; Chapin, J.K.; Kim, J.; Biggs, S.J.; Srinivasan, M.A.; Nicolelis, M.A.L. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408 (2000), 361-365.
 
     
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