This paper presents experimental evidence to support the feasibility of an evoked-potential vision-tracking system. The topics discussed are stimulator construction, verification of the photic driving response in the electroencephalogram, a method for performing frequency separation, and a transient-analysis example. The final issue considered is that of object multiplicity (concurrent visual stimuli with different flashing rates). The paper concludes by discussing several applications currently under investigation

Skidmore, Trent A.

English

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ESTABLISI-IING AN EVOKED-POTENTIAL VISION-TRACKING SYSTEM 
Trent A. Skidmore 
Ohio University 
Athens, Ohio 
SUMMARY 
This paper presents experimental evidence to support the feasibility of an evoked-potential vision- 
tracking system. The topics discussed are stimulator construction, verification of the photic driving 
response in the electroencephalogram, a method for performing frequency separation, and a transient- 
analysis example. The final issue considered is that of object multiplicity (concurrent visual stimuli with 
different flashing rates). The paper concludes by discussing several applications currently under 
investigation. 
INTRODUCTION 
The primary use of visual evoked potentials (VEPs) has been in clinical applications (refs. 1-2). 
However, because VEPs involve conceptually simple instrumentation, are relatively safe, and inherently 
noninvasive, they are worth investigating for possible use in other applications. One such a.pplication 
would be an evoked-potential vision-tracking system. To demonstrate the feasibility of such a system, it 
will be shown that distinct frequency-separable components can be evoked in the scalp potentials 
recorded using conventional electroencephalographic (EEG) techniques. 
THE STIMULATOR 
A flexible stimulator was needed to investigate the issues involved in an evoked-potential interface. 
To realize the stimulator, a personal computer was used, equipped with the C programming language, a 
high-resolution video card and monitor. Several programs were generated that could flash a white object 
(either a square or arrow) on a black background at a specified rate. Computer hardware constrained the 
flashing rates to be integer submultiples of the 70.1 Hertz (Hz) screen update rate. This led to the four 
primary frequencies being 35.050,23.367, 17.525, and 14.020 Hz. While slower flashing rates were 
possible, it was deemed that, at least for this initial investigation, a minimum of 14.020 Hz should be 
imposed. This was based primarily on the need to avoid the alpha band (8-13 Hz) and to minimize 
viewer irritation. 
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To ensure that the stimulator was performing as expected, data were collected using a photo 
darlington transistor. With the photo transistor taped to the monitor (stimulator), a 1" by 1" white square 
was flashed against a black background. For each of the chosen frequencies, the output of the photo 
transistor was seen to be a periodic train of pulses at the expected rate. Spectral analysis showed that 
each stimulating frequency produced a component at the fundamental as well as at higher harmonics. 
Thus, based on these findings, it was concluded that the stimulator was performing correctly, generating 
driving frequencies at the desired rates. 
VERIFYING THE PHOTIC DRIVING RESPONSE 
To verify that the evoked potentials were detectable in the EEG, data were collected under five test 
conditions. These included flashing the 1" by 1" white square (at a viewing distance of 36") for two, 
sixteen-second runs at each of the rates listed above (35.050, 23.367, 17.525 and 14.020 Hz). In 
addition, to fiorm a control set, data were collected for a run in which the subject looked at an illuminated 
black screen with no flashing object. The EEG was recorded (at a rate of 256 samples/second) over the 
0 1 and 0 2  locations, each referenced to CZ, according to the International 10-20 System of Electrode 
Placement. Sample spectra from the 35.050-Hz and 23.367-Hz runs are shown in figure 1, with the 
stimulating frequencies circled. To generate these plots, a hybrid periodogram was calculated. This 
periodogram averaged the spectra from both electrode pairs over two separate sixteen-second runs. This 
was done to highlight the peaks and to effectively reduce the surrounding noise. It is, however, crucial to 
note that the desired frequency peaks were also visible in the unaveraged spectra. The periodogram was 
performed merely to enhance the photic driving effect. 
FREQUENCY SEPARATION 
To show that an evoked-potential interface is possible, it is important to verify that the four 
driving rates are distinguishable. To demonstrate this, sixteen seconds of 01-Cz data for each rate were 
passed through four separate band-pass filters. The filters were fourth-order, infinite-impulse response 
Butterworlh filters implemented in the LabVIEW programming language. The bandwidth of each filter 
was 1 Hz, with a center frequency corresponding to each driving rate (35.050,23.367, 17.525 or 14.020 
Hz). The spectral output of two of these filters is shown in figure 2. The legend indicates the notation 
used for each photic driving frequency. In both plots it is clear that the particular driving frequency 
being tested produces the largest spectral output. For example, in the top graph, data from the four rates 
were passed through the 35.050-Hz filter. As can be seen, the run containing the 35.050-Hz driving 
frequency, marked with the solid squares, has the largest spectral component at the output of the 35.050- 
Hz band-pass filter. In every case, each driving frequency produced the largest response in its associated 
filter. 
From these results it is apparent that band-pass filtering can be used to detect the dominant frequency 
in the steady-state evoked potential. While it is beyond the scope of this paper to discuss frequency 
detection schemes in detail, it has been demonstrated that separation and detection of the dominant 
photic driving frequency are possible. 
TRANSIENT ANALYSIS 
TO demonstrate the transient nature of the eyelbrain system as the frequency of a single stimulating 
object is varied, it was necessary to devise a method for measuring time-dependent frequency changes. 
One manner of doing this is through the use of the spectrogram technique. To understand the operation 
of the spectrogram, suppose that a signal consists of the following components, 
0 c t < 4 seconds 
4 c t < 8 seconds 
8 < t < 12 seconds 
12 < t < 16 seconds 
16 < t < 20 seconds 
20 < t < 24 seconds 
24 < t < 28 seconds 
28 < t < 32 seconds 
and is sampled at 256 samples/second. A spectrogram of this signal is shown in figure 3. The plot 
consists of thirty-two one-second records or epochs. Each one-second record is the Fourier transform of 
a one-second block of time. The height of each record represents a linear measure of the spectral content 
during that particular time interval. The units on the horizontal axis are Hertz and the frequency 
binwidth (resolution) is 1 Hz. Observe from the example how the spectrogram plot "hides" lines of 
lower magnitude behind peaks that occur earlier in time. This convention allows time-dependent spectral 
trends to be observed while minimizing plot clutter. 
The results of applying the spectrogram technique to demonstrate time-dependent spectral changes 
afe highlighted in figures 4 and 5. Data for this test were collected when the stimulator was alternately 
flashing a 35.050-Hz and 23.367-Hz 1" white square on a black background. The stimulator initially 
displayed a brief message instructing the subject to watch the square. The stimulator then alternated 
between the two frequencies every five seconds. A total of forty-five one-second records a:re represented 
in the figures. The data was recorded over 0 1  with respect to C,. 
To generate the spectrogram and to effectively illustrate the transitions between frequencies, the data 
were first passed through a parallel-filter network. This network consisted of two band-pass filters 
centered at 35.050 Hz and 46.733 Hz. The bandwidth of each filter was 4 Hz. The 35.050-Hz filter was 
used to isolate the 35.050-Hz component and a 46.733-Hz filter was used to isolate the 23.367-Hz 
component. The reason for using the 46.733-Hz filter is twofold: For this subject, the 23.367-Hz 
stimulator actually produced more distinct spectral peaks at the first harmonic (46.733 Hz) than at the 
fundamental. Furthermore, because of the 1-Hz binwidth limitation of this particular test, the 46.733-Hz 
filter captured the 23.367-Hz stimulator effect better than a 23-Hz filter. 
To further illustrate the transient effects, consider figure 5. These graphs show two frequency-slices 
from the spectrogram of figure 4. The top graph illustrates the spectral changes in the 35-Hz bin. The 
bottom graph illustrates the spectral changes in the 47-Hz bin. The numbers placed directly on the 
graphs indicate the fundamental stimulating component in that time range. For example, for 1 1 t 1 5  
seconds, the stimulator was flashing 35.050 Hz. For 6 1 t I 10 seconds, the stimulator was flashing 
23.367 Hz, and so on. From these plots it is clear that the evoked-potential method can track time- 
dependent spectral changes as perceived by the eyelbrain system. Furthermore, it is apparent that 
harmonics of the stimulating frequency are also of importance and may be of use in certain detection 
schemes. 
OBJECT MULTIPLICITY 
Object multiplicity is the problem of having more than one flashing region within the field of vision. 
In order to achieve a viable interface, it is necessary for the eyelbrain system to be able to distinguish 
between various flashing objects. To investigate this concept, a simple test scenario was devised. In this 
test, the monitor was made to flash two horizontal arrows, one at 35.050 Hz and the other at 23.367 Hz. 
Figure 6 sho,ws the dimensions and spacing of the arrows. The viewing distance was again 36". Figure 7 
shows the spectral results when the horizontal separation {s} of the arrows was 3.5". For the upper 
graph, the subject was instructed to look at the 35.050-Hz arrow (placed at screen right). As expected, 
there is a large spectral peak at this frequency and no significant 23.367-Hz component. The lower graph 
shows the spectrum generated when the subject was focused on the 23.367-Hz flashing arrow. Note that 
the dominant peak is again at the driving frequency under observation. 
To further test the object-multiplicity issue, the arrows were moved to a separation of only 1.75". 
The results firom this test were similar to those described above. As a final test, the arrows were flashed 
with no horizontal separation {s=OM}. Again, the driving frequencies were surprisingly distinguishable. 
Therefore, based on these results, the eyes appear to be able to focus on a region that is sufficiently small 
so as to allow this type of system to be used in a direct human-computer interface. 
APPLICATIONS 
There arc: many possible applications for an evoked-potential vision-tracking system. Two 
applications that are currently being investigated include a two-dimensional directional controller (fig. 
8), and a moving-target tracking system (fig. 9). In the figure-8 scenario, the user observes and watches 
a stimulator {:hat is displaying a four-amw arrangement. Each arrow is flashed at a different frequency. 
This is done in order to evoke a frequency-separable photic driving response that is dependent upon 
which m o w  the subject is looking at. For example, if the subject is focusing on the "up" arrow, flashing 
at a frequency of f (flashes per second), then an fl-Hz component should manifest itself in the EEG. 
Subsequently, if the user changes focus from the "up" arrow to, say the "right" arrow, then the primary 
frequency being driven into the EEG will be changed from f l  to f2. Using a detector that would 
recognize these frequency changes, a user could systematically focus on a particular sequence of arrows 
to perform a two-dimensional control task, such as moving a computer's mouse. 
Figure 9 illustrates one possible realization of a moving-target tracking system. For this,, the user 
would watch a monitor (stimulator) displaying, for example, several moving airplanes, each flashing at 
different rates. Upon determining which airplane the user was focused on, the computer would highlight 
that plane. Based solely on the photic driving response of the highlighted object, the applica.tion would 
be capable of tracking the eye's movement as the airplane (target) moves across the screen. 
CONCLUSIONS 
Several important issues have been discussed that relate to the feasibility of an evoked-potential 
vision-tracking system. It has been shown that the photic driving response is apparent in the spectra of 
the EEG and is distinguishable through band-pass filtering. It has also been shown that the transient 
properties of the photic driving response are well defined, indicating that the eyelbrain system is capable 
of discerning time-dependent frequency changes. Finally, it has been demonstrated that dual stimulating 
objects also appear to generate responses that are unique and separable. Based on these results, a 
conceptual foundation for a vision-based interface has been established. 
ACKNOWLEDGEMENT 
The work reported here was in part funded through NASA Grant NAG 1-940 and is the subject of a 
patent disclosure (No. 18/4/91) filed at Ohio University April 9, 1991. 
REFERENCES 
1. Barber, C. and Blum, T. eds.: Evoked Potentials I11 - The Third International Evoked 
Potentials Symposium, 1987. 
2. Bodis-Wollner, I. ed.: Evoked Potentials, Annals of The New York Academy of Sciences, 
Vol. 388, 1982. 
frequency (Hz) 
Figure 1. Photic Driving Response at 35.050 and 23.367 Hz. 
Legend *14.020Hz -23.367Hz 
-at 17.525 Hz + 35.050 Hz 
frequency (Hz) 
Figure 2. Frequency Separation Using Band-Pass Filtering. 
frequency (Hertz) 
Figure 3. Spectrogram example. 
frequency (Hertz) 
Figure 4. Spectrogram for the output of 
35- and 47-Hz band-pass filters. 
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Figure 6. Object Multiplicity Test. 
frequency (Hz) 
Figure 7. Object Multiplicity at 35.050 and 23.367 Hz. 
Figure 8. Four-arrow evoked-potential interface. 
Figure 9. The tai-get-tracking evoked-potential interface. 


Establishing an evoked-potential vision-tracking system

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