COMPUTER-CONTROLLED NEUROSTIMULATION

FOR A VISUAL IMPLANT

S. Romero

Department of Computer Science, University of Jaén, Campus Las Lagunillas s/n, Jaén, Spain

C. Morillas, F. Pelayo

Department of Computer Architecture and Technology, University of Granada, Granada, Spain

E. Fernández

Institute of Bioengineering, University Miguel Hernandez, Elx, Spain

Keywords: Artificial vision, electrical neurostimulation, microelectrodes, active implants, phosphene, neuroprosthesis.

Abstract: Current research in therapies for restoring a functional form of sight to the blind includes interfacing

electronic neurostimulators with some point of the visual pathway. This approach requires controlling a

number of waveform parameters which might vary for every implanted patient and for every channel in an

interface that may have hundred or thousands of electrodes. Therefore, the clinical, acute research stage of

the implant should be controlled in a flexible and easy way, in order to obtain the information that will lead

to a chronic implantable device. We describe such a system, based on a PC connected to an electronic

neurostimulator, which delivers bi-phasic pulses to a set of implanted microelectrodes. This platform

performs an automated patient-driven procedure to find stimulation thresholds. The system implements a set

of physchophysical tests in order to determine the properties of the elicited visual perceptions, and applies

an automatic re-mapping of the electrodes to obtain better recognizable patterns of percepts. Our platform

can interface some other tools oriented to obtain, in a next research stage, a portable and chronic version of

the visual implant.

1 INTRODUCTION

World Health Organization estimates that about 37

million persons are completely blind, while those

affected by low vision sum up to 124 million (WHO,

2005). These numbers are increasing due to the

ageing of population in developed countries, and to a

variety of pathologies and accidents affecting one or

more of the components of the complex visual

system.

Major causes of blindness are age-related

macular degeneration (AMD), diabetic retinopathy,

glaucoma or traumatic damage.

Therapeutic choices for blindness might be as

varied as its causes. Clinical treatments are available

for some kinds of visual impairments, as the ones

caused by cataracts. However, a strong research is

undergoing for other types of visual pathologies, for

which no clinical solutions are available yet.

These research lines include retinal cell

transplantation, the use of growth factors, or gene

therapy, mainly applied to retinitis pigmentosa (RP).

Apart from biological approaches, a number of

research groups are working towards the

development of visual prostheses, which would

replace one or more of the damaged stages of the

visual pathway, providing a rudimentary, but

functional, form of visual perceptions.

Depending on the point of the visual pathway on

which the neurostimulation interface is placed, we

can classify visual neuroprostheses as retinal

(Humayun, 2003), optic nerve (Veraart, 1998), or

cortical implants (Dobelle, 2000; Troyk, 2003;

Fernández, 2005).

Romero S., Morillas C., Pelayo F. and Fernández E. (2008).

COMPUTER-CONTROLLED NEUROSTIMULATION FOR A VISUAL IMPLANT.

In Proceedings of the First International Conference on Biomedical Electronics and Devices, pages 84-91

DOI: 10.5220/0001051600840091

 SciTePress

In retinal prostheses, the set of electrodes are

implanted below or onto the retina, in order to

replace the role of photoreceptors, or ganglion cells,

respectively. In the case of optic nerve implants, a

cuff electrode is placed around the bunch of axons of

the ganglion cells connecting the output of the retina

to the next stage of the visual pathway. In cortical

implants, electrical pulses are directly delivered to

the visual area of the brain cortex, by using surface

planar electrodes, or penetrating tips.

Whatever is the selected interface for visual

neurostimulation, the employment of this kind of

devices implies a high degree of complexity.

The amount of channels in the different

prototypes used in research range from 16 to 100

electrodes (Normann, 1999), although some studies

have shown that a number between 600 and 1000

electrodes would be required to obtain an adequate

performance in basic tasks, such as object

discrimination, recognition of big characters or

pedestrian navigation (Cha, 1992).

The signal delivered to every electrode is a bi-

phasic charge-balanced pulse, and includes a set of

parameters such as phase width, pulse duration,

pulse current amplitude, number of pulses in a train,

inter-pulse interval, inter-train interval, etc. The set

of values for these parameters might vary from

channel to channel, and are expected to be different

for every implanted individual (see Fig. 1).

This way, the process of tuning all the

parameters for the prosthesis after safe implantation

is a complex and lengthy task, which is unavoidable

in the research to determine the feasibility of a

neurostimulation-based visual prosthesis.

In this paper, we describe a computer-based set

of software and hardware conceived for research

with visual neuroprostheses. Our platform is mainly

oriented to test cortical implants, but it is easily

extendable for other types of implants.

The purpose of the research platform is to

provide automated and patient-driven procedures for

prosthesis parameter tuning and psychophysical

testing. The computer-controlled neurostimulator

serves as an abstraction layer to hide the complexity

of handling such an intricate implant.

The platform is part of a set of tools designed to

cover different needs in the development of a full

visual prosthesis, such an artificial retina model, or

an automated synthesizer for embedded circuits to

obtain a portable, low power consumption controller

for the stimulator.

Figure1: Biphasic pulse train for cortical neurostimulation.

Pulse trains contain a number of parameters that can be

selected, as amplitude (A), pulse width (PW), inter-phase

interval (IPhI), interpulse interval (IPI), train length (TL),

and inter-train interval (ITI).

2 A VISUAL PROSTHESIS

MODEL

The platform described in this paper has been

developed to assist in the post-implantational stage

of research of a visual neuroprosthesis project. The

whole project, known as CORTIVIS (Cortical

Visual Neuroprosthesis for the Blind) (CORTIVIS,

2002), has been carried out by a consortium of seven

research labs and a small company under European

funding (see Fig. 2).

Figure 2: Scheme of the visual prosthesis proposed by

CORTIVIS. A camera grabs images, which are processed

by a bioinspired encoder. The encoder sends stimulation

commands wirelessly to the intracranial telemetry system.

Finally, the array of microelectrodes stimulates the visual

cortex of the subject.

The model selected for the CORTIVIS prosthesis

includes one or two cameras, as input, which feed a

bio-inspired retinal encoder, which partially replaces

the role of the visual processing taking place at the

retina, and determines the moment in which specific

implanted electrodes should be activated. The output

COMPUTER-CONTROLLED NEUROSTIMULATION FOR A VISUAL IMPLANT

of this stage is an address-event representation

(AER) indicating the number of electrode which will

be stimulated. This stream of addresses is sent

through a wireless link to the implanted section of

the prosthesis. The RF link also provides energy for

the implanted stimulator. This neurostimulator is

finally connected to an array of microfabricated

electrodes, which are inserted into the visual area of

the brain cortex. In our case, the Utah Electrode

Array (Normann, 1999), bearing 100 electrodes, has

been selected as the neuroelectrical interface.

3 RESEARCH PLATFORM

In this section, we describe the organization,

operation modes and capabilities of the research

platform we have developed for the testing and

tuning of cortical visual neuroprostheses.

3.1 System Architecture

Fig. 3 shows the building blocks that integrate the

experimenting station. A PC which runs the software

required to control the platform is connected to an

electronic neurostimulator. The connection is made

through one of the computer ports. Initially, we

employed the LPT port. However, the second

version of the neurostimulator is using a USB port to

exchange information with the PC. An opto-

coupling stage protects the patient against electrical

risks, as required for biomedical instruments.

The second stage of the platform is an electronic

equipment which receives and decodes commands

from the PC, according to a pre-established protocol.

This neurostimulator can receive configuration,

stimulation and test commands. Whenever a

configuration word is received, it stores the

waveform parameters for the corresponding channel

in a configuration memory. If a stimulation

command is sent from the PC, the equipment selects

the corresponding output channel through a

demultiplexor, and drives a Digital-to-Analog

converter so that a biphasic waveform is sent to the

output, according to the stored parameters for the

corresponding channel. Test commands just check

the state of the electronics, in order to detect

malfunctioning electrodes (due to encapsulation,

breakage during insertion, etc.).

The last block in the platform is the intra-cranial

implant, which is connected to the output of the

neurostimulator. In our case, we have selected the

Utah Electrode Array, which is a microfabricated

array of 10x10 microelectrodes (Fig. 4). This array

is pneumatically inserted into the brain cortex, so

that the tips of the electrodes are expected to reach

layer IV of the visual cortex. Previous experiences

have shown that electrical stimulation of cells in this

layer evoke visual percepts, similar to stars in the

night, which are called “phosphenes” (Schmidt,

1996).

In this acute clinical version of the CORTIVIS

prosthesis, a set of wires is used to connect the

stimulation equipment to the implant, discarding for

later use the radio-frequency link.

Figure 3: Structure of the research platform. A PC runs a

control software, and sends configuration and stimulation

commands through a PC port. An optocoupling stage

protects the patient against electrical risks. The next block

is the neurostimulation electronics. A configuration

memory stores the waveform parameters for every

channel, and a demultiplexing and digital-to-analog

convertion block issues the corresponding waveform, and

sends it to the proper electrode in the array.

Figure 4: the Utah Electrode Array (UEA). It is a 10x10

electrode matrix, bearing 1.5 mm tips, separated by 400

microns. It is microfabricated in silicon and platinum

(Normann, 1999).

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

3.2 Operation Modes

The experimental set-up can present two different

configurations, called “stimulation” and

“simulation/training” modes.

The neurostimulation configuration corresponds

to the set described in section 3.1, that is, a PC

controlling a neurostimulator, which delivers pulses

to an implanted array of electrodes. The purpose of

this set is to allow researcher tuning the set of

parameters required to elicit phosphenes in the

visual field of the patient, and then, run a series of

psychophysical tests, in order to characterize the

evoked perceptions.

However, an alternative configuration is

available for debugging and training purposes (with

sighted volunteers). In this second choice, the

electronic neurostimulator and the implanted array

of electrodes are replaced by a second PC with head-

mounted displays. The first PC plays the same role

as in the previous configuration. The commands sent

through the communication port are received by the

second PC, which implements simulation rules

including random values for current threshold, and

phosphene location in the visual field. The simulator

in the second PC leads to a representation of a set of

phosphenes in a head mounted display, according to

the information obtained in similar experiences with

human visual intra-cortical microstimulation.

4 SOFTWARE CONTROL

The platform described in the former section runs a

program written in C++, under Microsoft Windows,

which controls all the automated procedures to be

carried out for stimulation parameter tuning and

psychophysical testing.

The control application, named “V1 Cortistim”

has a graphical user interface that allows the

experimenter to select every stimulation parameter

for the waveform, and run or stop every test.

However, in order to accelerate the lengthy process

of tuning the stimulation parameters for each

implanted electrode, and executing an extensive set

of psychophysical essays, every of these procedures

have been automated. This way, the patient becomes

the operator of the system, setting the pace of the

experimenting steps, and avoiding verbal interaction,

so the feedback given by the implanted individual by

means of the computer input mechanisms, is

automatically recorded, launching the next action of

the process. In the following sections, we detail the

procedures that are implemented in the research

platform.

Figure 5: V1 Cortistim Graphical User Interface, allowing

to control every waveform parameter for every channel in

the microelectrode array.

4.1 Current Threshold Finding

The first task after the patient has been safely

implanted is finding the lowest current amplitude

required to evoke a phosphene. This procedure has

to be done for every channel of the implant. This

way, the objective is to have as many phosphenes as

possible forming patterns of percepts, but injecting a

minimum amount of charge into the cortical tissue.

As mentioned before, this procedure is patient-

driven, so the response of the patient triggers the

next step of the process. The basic algorithm selects

every channel, and issues pairs of configuration and

stimulation commands to the stimulator with

increasing current amplitude, until the patient signals

the occurrence of a phosphene in his/her visual field,

by clicking a mouse button. Then, the process is

repeated for the next electrode.

We have included two modifications to this basic

search algorithm to reduce the number of total steps

required to complete the process. We have to take

into account that in a near future, next generations of

implants might include an amount of electrodes over

the thousand, and for each electrode a set of current

values should be tested, leading to a very tedious

and lengthy process. The first modification is

employing a binary search scheme, instead of a

linear model, reducing the complexity of the

problem. The second enhancement takes into

COMPUTER-CONTROLLED NEUROSTIMULATION FOR A VISUAL IMPLANT

account that current thresholds are expected to

gather around a mean value. Having this, we set the

starting point for the binary search for a channel to

the threshold found for the previous channel.

Applying this procedure, a set of 100 electrodes

can be configured in less than 5 minutes (for a step

of 1 second between consecutive stimulations).

A similar scheme can be applied to the rest of

parameters of the stimulation waveforms, although

most experimental implants take amplitude as the

main parameter. In any case, all the parameters are

interrelated, as they influence the amount of charge

injected, which is the main responsible for

phosphene evocation.

Experimental results of using the platform to

generate biphasic stimulation pulses are exposed in

Fig. 6 and in Fig. 7.

Figure 6: Example of biphasic pulse obtained with the

experimental neurostimulation platform (vertical scale:

500 mV/div; horizontal scale: 200 microsec/div).

Figure 7: Example of pulse train obtained with the

experimental neurostimulation platform (vertical scale:

100 mV/div; horizontal scale: 500 microsec/div).

4.2 Psychophysical Tests

After the threshold current has been determined for

every channel, a set of tests is required to be carried

out in order to characterize the psychophysical

properties of the evoked percepts.

This way, an extensive set of perceptual tests has

to be run, which again requires making this process

as easy and agile as possible. Following the same

philosophy as for the threshold finding procedure, a

patient-driven automated scheme is again employed.

The V1 Cortistim platform provides the

following set of psychophysical essays:

 Brightness sensitivity: a change in certain

parameters of the waveform (mainly

amplitude) will modify the perceived

brightness of the evoked phosphene. A pair of

phosphenes is elicited, and the brightness of

one of them changes until the patient finds no

change.

 Spatial resolution: a pair of phosphenes

produced by distant electrodes is evoked

consecutively with closer and closer

electrodes until the patient cannot differentiate

them.

 Phosphene cluster count: a set of 1, 2 or 3

phosphenes from adjacent electrodes is

elicited. The patient gives feedback on the

number of phosphenes perceived.

 Motion mapping and orientation selectivity: a

straight line of electrodes (row, column or

diagonal) in the matrix consecutively get

activated. The patient indicates the general

direction of apparent motion of the phosphene.

 Simple pattern discrimination: a simple pattern

(similar to Snellen symbols) and its

“mirrored” pattern are consecutively activated

in the electrode array. The subject tells if they

seem to be different or similar.

5 PHOSPHENE MAPPING AND

RE-MAPPING

A key aspect in the design of a phosohene-based

visual neuroprosthesis is the ability to evoke patterns

of percepts that can be matched to known models

from the visual world.

Experiments both with human and non-human

subjects have shown that the correspondence

between the spatial location of the stimulation point

in the cortex and the position of the evoked

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

phosphene in the visual field can present strong

deformations (Normann, 2001).This is especially

remarkable for high density arrays of electrodes, in

which the correspondence between the stimulation

and the perceptual spaces is highly non-linear and

non conformal. This fact might be caused by the

complex interconnections among the neural cells

that respond to stimulation in the area of influence of

an electrode. Anyway, a mapping between the

location of the activated electrode and the position

of its corresponding phosphene in the visual field

should be built for every channel of the implant.

Correspondingly, an inverse transformation or re-

mapping, indicating which electrodes should be

activated to get a specific pattern of phosphenes is

required in order to evoke recognizable percepts. We

describe the solutions implemented for our

experimentation platform both for the mapping and

re-mapping objectives.

5.1 Phosphene Mapping

Several mapping methods have been used for

building a table to determine the spatial coordinates

of a phosphene corresponding to the activation of

every electrode, as joysticks, dartboards or digital

tablets. Our objective in this procedure is not only,

as before, to obtain an agile system by avoiding

verbal interaction, and by having an automated

patient-driven process, but also achieving precision

in a process that is very prone to inaccuracy.

Our platform includes a mapping process based

on a tactile screen placed just in front of the patient,

as exposed in Fig. 8. A consecutive pair of

phosphenes is elicited, and the patient touches the

tactile screen in the points in where the percepts

appear on the visual field. The platform, in training

mode, is able to find out the mapping error, as the

real location of the evoked phosphenes in the head

mounted displays is computer-generated, and can be

compared to the position pointed out by the subject.

Figure 8: Example of usage of the automated phosphene

mapping system. The platform is being used in training

mode. A sighted volunteer wearing Head Mounted

Displays perceives computer-generated phosphenes, and

indicates their location on his visual field by touching a

tactile screen in front of him.

5.2 Re-mapping Procedure

Once an electrode-to-phosphene map is available, a

pattern of phosphenes can be elicited by stimulating

the corresponding electrodes. So, whenever a

specific distribution of phosphenes is required in the

visual field of the patient, a list of electrodes has to

be determined. This process is called re-mapping.

The map of phosphenes elicited with intra-

cortical microstimulation appears to be stable for a

given patient (Schmidt, 1996). However, there are a

limited number of phosphenes available in specific

locations of the visual field, which have to be used

to evoke any desired pattern.

Our first approach is to project the desired

pattern on the center of the visual field, and then,

select, for every desired point, the closest phosphene

to it. With a reverse look up at the mapping table, its

corresponding electrode is found.

Instead of selecting the absolutely closest

phosphene in the map to the desired point, we

choose the closest phosphene which hasn’t already

been selected. That way, we can obtain patterns

including a maximum number of phosphenes, rather

than having more precise locations with less

percepts. Although the patterns can present some

more deformation, its completeness, along with the

training of the patient, is expected to lead to a better

recognition, as illustrated in Figs. 9 and 10.

Additionally, this selection procedure enhances

the response whenever the distribution of the map is

highly uneven. So, in the case we have a region of

the visual field covered by a small group of

phosphenes, and another region with a high density

of percepts, a moving object in the visual field

should be composed of the same number of

phosphenes. Direct selection of the closest

phosphene would lead to a different number of

points in a pattern, depending on the location of the

object in the visual field (which makes difficult, for

example, recognize a moving object as a unit). With

our algorithm, an object is always composed of the

same number of phosphenes, regardless of its

location in the visual field. The shape of the pattern

can vary, in an effect similar to looking a moving

objective through a frosted glass.

COMPUTER-CONTROLLED NEUROSTIMULATION FOR A VISUAL IMPLANT

Figure 9: Phosphene pattern that would be elicited after

direct selection of the top row and central column of an

electrode array. Although the distribution of electrodes

forms a “T” shape, the evoked pattern is unrecognizable,

so a remapping is required. This set of phosphenes

corresponds to a randomly generated mapping (25x25).

Figure 10: after our remapping algorithm is applied to the

previous figure, a different set of electrodes are activated,

yielding a better recognizable pattern of phosphenes,

closer to the desired “T” shape.

6 ADDITIONAL TOOLS FOR

VISUAL NEUROPROSTHETICS

The platform described in this paper is a specific

design for the clinical testing stage of a complete

visual neuroprosthetic system.

However, corresponding to the whole system

architecture depicted in Fig. 2, some other relevant

blocks are required for achieving a complete,

portable visual prosthesis.

Regarding this point, we give a brief reference of

additional platforms and hardware/software tools

developed to contribute to the complete prosthetic

system. Details of every one of them can be found

elsewhere.

Direct stimulation of the visual cortex requires,

somehow, replacing the image processing carried

out by earlier stages of the visual pathway, such as

the spatio-temporal filtering performed by the retina.

For this purpose, a retina-like processing

software platform has been developed in Matlab,

which allows experimentation with an extensive set

of parameters, so that a video or live camera capture

can be processed, and the electrode firings

(corresponding to the activity of retinal ganglion

cells) are obtained. This way, the response of our

artificial retina can be compared to the one given by

biological retinae when exposed to the same stimuli.

Further information can be found at (Pelayo,

2004).

A second objective of the CORTIVIS project is

to achieve a portable, low power consumption

version of the previous retinal pre-processor, so that

the patient can wear a camera mounted on

eyeglasses frame, and the processor will transmit

activation commands to the corresponding channels

of the intra-cranial segment of the implant via a

wireless link.

A plug-in module for the Retiner program has

been built, which is able, to translate the retinal

model designed with our software into a

configuration file for a programmable logic chip, so

that all the retinal processing is carried out by a

single, portable integrated circuit. References can be

found at (Martínez, 2005).

7 CONCLUSIONS

We present a computer-controlled platform

conceived to control a neural interface. The main

objective is to provide a friendly and automated way

of performing experiments after implantation of an

array of microelectrodes into the visual cortex of a

patient. The platform serves as interface to handle

the complexity inherent to a multi-channel brain-

computer link that requires tuning biphasic

stimulation pulse trains for every electrode.

Every experimental procedure is automated, and

patient-driven, in order to make the tuning and

testing process as fast as possible. The platform

includes a set of psychophysical tests to determine

key features of the electrically evoked percepts.

As previous micro-stimulation experiences

confirm, the elicited patterns of phosphenes suffer

strong deformations with respect to the distribution

of the corresponding electrodes in the array. As the

objective is to evoke recognizable patterns, a re-

organization (re-mapping) between the stimulation

and perceptual spaces is required. Our platform

includes a re-mapping algorithm for such a purpose.

We also make a brief reference to some additional

tools developed by our research group, also

BIODEVICES 2008 - International Conference on Biomedical Electronics and Devices

contributing to the development of a complete

independent prosthetic system. These tools include a

flexible retinal processing model, an automatic

synthesizer to program integrated circuits for retinal

processing, and a system to include binocular and

spatial information in the set of stimuli sent to the

brain.

Unfortunately, it is difficult to provide a detailed

and standardized comparison against other systems

under development. On one hand, these kinds of

systems are specifically designed and fitted to

control a particular implant, so no compatibility

criteria are considered. On the other hand, as

neuroengineering is a young field of research, no

standards for measuring and comparing the

performance of a prosthetic system are available.

Nevertheless, some relevant organizations

involved in blindness and low vision research, as the

ARVO (ARVO, 2007), or the Smith-Kettlewell Eye

Research Institute (SKERI, 2007), are organizing

and conducting specific meetings aiming to arrive to

a standardized set of tests that will be useful to

provide a measurement of the performance of these

implants.

ACKNOWLEDGEMENTS

This work has been carried out with the support of

the European project CORTIVIS (ref. QLK6-CT-

2001-00279), the National Spanish Grants

DEPROVI (ref. DPI 2004-07032), IMSERSO-

150/06, and by the Junta de Andalucía Project: P06-

TIC-02007.

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COMPUTER-CONTROLLED NEUROSTIMULATION FOR A VISUAL IMPLANT