Wetware? [references]

Brooke Paul bpaul at banting.bio.uci.edu
Thu May 12 15:33:24 EST 1994


	Here is an article that was posted to this group a while ago.  I 
happened to have kept it.  It is not mine, and I take no credit or 
responsibility for its contents.  Of particular interest to this 
discussion is the work by Fromherz, et al.  If you're not into reading 
long posts, and want to get to the primary literature then check in your 
local library for his work.

bionet.neuroscience  #812                                                   
From: 93asw at williams.edu (Andrew Wright)
[1] Brain-Computer Interface, long
Followup-To: bionet.neuroscience
Date: Sun Dec 06 18:27:26 PST 1992
Organization: Williams College
Lines: 192
Distribution: world
NNTP-Posting-Host: mac_ccc_si.cc.williams.edu

On Designing a Brain-Computer Interface:
After all, computers were once science fiction, too.

Andrew Wright
NSCI 401
Williams College
11/20/92
93asw at williams.edu
awright at mindvox.com

Note1: This paper is copyright 1992.  I am making this paper available
becuase of a large interest in the topic among people on the net.  Since
this is an academic paper, I am making the assumption that people out there
will be ethical enough not to pass this work off as their own.  To that
end, feel free to disseminate this and use it in your own research, but
please make sure to cite your source.  Thanks - Andrew.

Note2: This is a loosely edited version of this paper, with much of the
technical detail left out.  A full version is available on request.  I
would also be willing to place it on a site allowing anonymous ftp, if
anyone knows of an appropriate site.

        A Brain-Computer interface is a staple of science fiction writing. 
In it earliest incarnations no mechanism was thought necessary, as the
technology seemed so far fetched that no explanation was likely.  As more
became known about the brain however, the possibility has become more real
and the science fiction more technically sophisticated.  Recently, the
cyberpunk movement has adopted the idea of Rjacking inS, sliding RbiosoftS
chips into slots implanted in the skull (Gibson, W. 1984).  Although such
biosofts are still science fiction, there have been several recent steps
toward interfacing the brain and computers.  Chief among these are
techniques for stimulating and recording from areas of the brain with
permanently implanted electrodes and using conscious control of EEG to
control computers.  Some preliminary work is being done on synapsing
neurons on silicon transformers and on growing neurons into neural networks
on top of computer chips.
        The most advanced work in designing a brain-computer interface has
stemmed from the evolution of traditional electrodes.  There are
essentially two main problems, stimulating the brain (input) and recording
from the brain (output).  Traditionally, both input and output were
handled by electrodes pulled from metal wires and glass tubing. []
(Pickard 1979). Using conventional electrodes, multi-unit recordings can
be constructed from mutlibarrelled pipettes.  In addition to being fragile
and bulky, the electrodes in these arrays are often too far apart, as most
fine neural processes are only .1 to 2 5m apart. [] It is difficult to
permanently implant such arrays, and consequently it is difficult to
directly study the brain as a function of animal behavior. []
        Pickard describes a new type of electrode, which circumvents many
 of the problems listed above.  These printed circuit micro-electrodes
(PCMs) are manufactured in the same manner of computer chips.  A design of
a chip is photoreduced to produce an image on a photosensitive glass
plate.  [] This is used as a mask, which covers a UV sensitive glass or
plastic film. A PCM has three essential elements: 1) the tissue terminals,
2) a circuit board controlling or reading from the terminals and 3) a
Input/Output controller-interpreter, such as a computer.  The circuit
board and computer are often located outside the skull, to minimize tissue
invasion, allow for long-term implantation and permit the electrodes to be
detached between trials (Kuperstein and Eichenbaum 1985). []
        In addition to the ability to make multiple, closely spaced
recordings, P[CMs] often outperform the traditional electrodes in a number
of electronic measures (Kuperstein and Eichenbaum 1985). A further
advantage of PRONG [a type of PCM] was it's continued functioning after as
many as four days implantation. [] PRONG was able to simultaneously make
10-11 recordings from one side of the electrode. 
        While it is tempting to see in PRONG the potential for permanently
implanted brain recording and stimulating devices in the manner of
cyberpunk fiction, more mundane if equally exciting applications of similar
technology are being found now.  A six channel PCM is being commercially
produced for use as an implant in patients who have lost hearing but not
functioning of the auditory nerve (Ineraid Multichannel Cochlear Implant).
[] This device allows for hearing and speech recognition, although there
are limits to the amount of information that can be extracted.  In
two-syllable recognition tests, scores range from 0 to 100%, with the
median being 44% (Dorman et al. 1991).
        Interestingly, these limits may not be inherent in the cochlear device,
but in the encoding algorithm.  Wilson et al. (1991) have designed a new
technique, CIS, which is presumably based on improved Digital Signal
Processing (DSP) capabilities. []  The increase in comprehension engendered
by this technique overwhelmed the sensitivity of the tests.  In some cases,
the subjects were well within the range of mild to moderate hearing loss.
[]
        Another possible use for PCMs is controlling robotic prosthetics.  A
special type of tissue terminal, an enclosure terminal, has holes in or
across conductors through which developing or regenerating neurons can
grow(Pickard 1979).  These are especially suitable to chronic preparation,
and could be implanted in the PNS where nerve regeneration is possible.
The chip could then interpret motor neuron signals for use controlling
prostheses.[] [PCMs] may even be useful in administering micro-doses of
ionotophoretic drugs (Pickard 1979).
        A fundamentally different approach to interpreting output from the
brain is the use of EEGs.  According to Wolpaw et al. (1991), "in theory
[the] brain's intentions should be discernible in the spontaneous EEG." 
However, the vast number of neurons and the complex structure of the brain
make such interpretation difficult if not impossible.  Therefore, efforts
have focused on training people to produce desired EEGs through
biofeedback mechanisms.  [] An immediate use for such a system can be seen
in providing a mechanism for communication between paralyzed patients and
the outside world through the computer. 
        The possibilities of interpreting EEG data and using it to control
computers have been brought to the consumer electronics front by the IBVA,
or Interactive Video Brainwave Analyzer (Nathan 1992).  A headband with
four adhesive electrodes sends data through a radio transmitter to a port
on a Macintosh personal computer.  The EEG is the filtered and run through
a fast fourier transform before being displayed as a three dimensional
graphic.  The data can then be piped into MIDI compatible music programs.
Furthermore, MIDI can be adjusted to control other external processes, such
as robotics.  The level of control provided by IBVA is limited at best and
the software does not actually interpret the brain's impulses.  Instead,
the user must program the software to interpret consciously determined
gross changes in the EEG.
        The interface between the brain and computers, either through
interpreting EEGs or through recording directly through PCMs is currently
limited by computing strength.  Conventional computers are well suited to
processing linear data, but only have limited application to more
distributed processes such as pattern recognition.  In order to address
these problems, neural net computers are modeled after the brain's complex
system of weighted synapses.  The strength of these neural nets can be
considered a function of the number of connections made between functional
units.  Computers are hampered by the limited number of connections imposed
by the constraints of processing time and memory space.
        To circumvent this, Masuo Aizawa is working on growing neurons
into neuralnet computers (Freedman 1992).  A neuron is capable of
processing many more inputs and outputs than a transistor, and is
obviously uniquely suited to neural net computing.  [] It is currently
possible to grow strips of interconnected neurons on [a semi-conductor
chip] the oxide [].  Of course, input to and output from the "chip" is
highly problematic.  Aizawa's solution is to divide the underlying
semiconductor into electrodes that can stimulate or record from the cells,
however he projects that this may take several years to develop (Freedman
1992). 
        Other researchers are working on ways to interface neurons
directly into silicon chips as well.  In a "first step toward multiple
recording in neurons and neural nets and toward the development of neural
biosensors and neuroelectronic circuits", Fromherz et al. grew neurons
into silicon insulated-gate Field Effect Transformers (FETs) (1991). []
        According to Fromherz et al., the neuron-Si junction, outperforms
metallicelectrodes, because of the capacitive coupling at high seal
resistance as attained by adhesion of the neuron to the gate without a
metallic conductor.  It is possible to construct patterns of such silicon
FETs on a single chip, allowing multiple recordings in a cell culture. 
There is an added advantage of the possibility of long term recordings at
high resolution and high signal-to-noise ratios (Fromherz et al. 1991). 
        If this type of neuron-Si junction could be integrated into printed
circuit micro-electrodes, a extremely versatile and functional interface
until could be constructed,  an improvement over metallic electrode PCMs.
If incorporated into enclosure type tissue terminals, researchers and
clinicians would have access to an interface between neurons and electronic
computers with high efficiency, conservation of information and one-to-one
neuron-electrode junctions.
        Immediate applications of this include increasing the functionality of
cochlear implants.  By increasing the number of electrodes, the efficiency
and specificity of the electrode-neuron interaction and the computational
power driving the electrodes, speech recognition could be enhanced even
further than currently possible.  This prefigures applications in which
PCMs are used to input data from computers directly into normal people's
auditory nerve, bypassing the actual production of sound by the computer.
Such functionality would be relatively easy to add by including an input
jack on the DSP chip of the implant.  A similar application could be found
in encoding visual stimuli directly into the optic nerve of blind people.
This could then be adapted to present computer generated visual signals
divorced from real world input.
        The auditory and optic nerves are perhaps the most accessible
methods of input to the brain.  Memory structures in the brain itself are
considerably less well understood.  However it is not outside of the realm
of possibility to directly include artificial memories through the use of
advanced PCMs implanted in the brain.  The problem of encoding information
in a manner recognizable to the human brain would be difficult to
surmount, especially given the possibility that data structures may be
encoded differently by each person.  [] It may be possible, then, to
tailor such neural nets to output data in a manner specific to one
person's internal representations of memories.  Although current
technology is probably incapable of such a feat, because of the incredible
amount of processing required, the combination of increased silicon chip
speed and the use of complex neural nets built from neurons could make
this possible. 
        The current approaches to interfacing the brain and computers described
above offer very concrete examples of utility.  Indeed, the potential for
even currently available systems is still unrealized.  However, it could be
argued that they are hampered by inherent limitations.  EEG based systems
have no possibility of input to the brain, and full comprehension of the
human EEG may be out of reach by even the most advanced imaginable
computers.  Similarly, PCM recording or stimulating devices may be limited
by both the size of the electrode arrays, the difficulty of implantation
and by the complexity of the brain.
        Despite optimistic projections, the brain's complexity may make it
impossible to directly access from a computer.  Even if these problems
could be overcome, there are still problems involved with the very idea of
implanting electrodes into the brain.  The potential damage to biological
structures may outweigh the tentative benefits of a direct computer
interface, especially in higher cortical structures.  Despite these
conceptual difficulties, the prospect of extending the inherent
capabilities of the organic brain allows the consideration of transcending
the limitations imposed by the corporeal body.  After all, computers were
once science fiction, too.

--
Brooke Paul                                       | bpaul at darwin
Laboratory of Cellular and Molecular Neurobiology |         .bio
UCI-Dept. of Psychobiology                        |         .uci
Irvine, Ca.  92717-4330                           |         .edu



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