Ординатура / Офтальмология / Английские материалы / Artificial Sight Basic Research, Biomedical Engineering, and Clinical Advances_Humayun, Weiland, Chader_2007

80 Walter et al.

Figure 4.4. These graphs show the results of the checker-placing task for all the test subjects. The bars show the percentage of the correctly placed checkers calculated with Eq. 3 and the dashed line represents the linear regression of the time. Board Top 16 was used only once at this stage of the experiment, so there is no standard deviation data available and thus no error bar. The number of repetitions for the other boards varied from test subject to test subject but was at least 3 times for every board and at the maximum 6 times (small numbers at the x-axis denote if there were more than three times and give the number of times).

at the start, but a slight drop in correct percentage is seen for boards with more white squares. This drop may be attributed to the increase in the number of white squares per board as well as to their positions on the boards: With more white squares on a board the squares were located more often at borders or in corners of the board as well as diagonally adjacent to another (see Figure 4.10, board designs). The regression time slopes show similar narrow intervals as in

Figure 4.3 for subjects 2, 4, and 5 (time/checker: 12.0–15.6 s), but the intercept for subject 5 (123 s) is much higher than that for the other two (26 and 34 s); this same subject also has much larger within session variability (error bars), although the same can be seen for a few board types (i.e. sessions) in subjects 2 and 4 as well. In most subjects, both mean time and variability drop below the regression line for the largest numbers of white squares, indicating the effect of practice reached by the last session of this experiment. This practice effect is also suggested by the high intercept values in subjects 1, 3, and 5.

Experiment 2

Experiment 2 was used to monitor an increase in subjects’ accuracy on the counting task. Boards were given to the test subjects in random order, for four consecutive test sessions. In addition to the prior instructions, subjects were allowed to start over if they felt that they had lost count; trials in which this happened were disregarded. The results of this task are depicted in Figure 4.5. The plots show the counting time as a function of the number of white squares. Similar to Experiment 1, a regression line was calculated from individual data points. In addition, 95% confidence interval bands for the regression line were added using Origin (Ver. 7.0, OriginLab Corp Northampton, MA). As can be seen from the time scale, all subjects have become significantly faster than in Experiment 1: Regression line slopes range from 0.5 to 1.2 s/square. Also the y-axis intercept is lower, at 5–17 s. Note that all five subjects now fit into these intervals, and that subject 1 (visually impaired) performs nearly as well as the other subjects. The regression lines provide a better fit to the mean values than those in Figure 4.3, and the SEM values are lower, suggesting that the subjects have a more even, i.e. practiced, performance.

Experiment 3

The purpose of Experiment 3 was to achieve proficiency in the placing task. The subjects placed the checkers on the boards without first counting the white fields, and boards were given to them in random order. The difficulty here was to coordinate hand and eye only by proprioceptive information because the hand was not directly visible in the pixelized view: Only the obstruction of the white border or white squares by the hand was noticeable. The possibility to re-start a trial was again provided. In this case the video image was switched off, all checkers were removed, and the orientation of the board was changed. Unlike in Experiment 1, subjects were always given 16 checkers. This was done to eliminate any possible cues about the number of white squares on the board. Figure 4.6 shows a test subject placing a checker and the corresponding pixelized camera image as it presented itself to the test subject at that moment. Figure 4.7 indicates the approach one of the test subjects developed for placing a checker. Other test subjects used different techniques. This experiment was more time consuming, and the number of trials per session varied from subject to subject,

82 Walter et al.

Figure 4.5. Results of the counting test. The boards were given randomly and so the number of times one board was used is different for every test subject.

ranging from 4 to 9 depending on the initial proficiency and further progress of the subject. The results of this experiment are depicted in Figure 4.8 (time vs. number of white squares) and 9 (points earned vs. number of white squares).

The regression line slopes for four subjects in Figure 4.8 are nearly identical: 9.58–9.73 s/checker. Only subject 3, with 7.8 s/checker, performs somewhat faster. The Y-intercepts range from 7.6 to 24.3 s, lower and more narrowly distributed than in Experiment 1. These data strongly suggest that during the 4 months it took to reach this point, all subjects reached very similar performance

Figure 4.6. Correspondent images of the board and the pixelized view presented to the test subject while one checker is placed on a white square.

levels, and times that depend primarily on the number of squares. Figure 4.9 shows that error rates for most subjects was consistently low even with a rigorous penalty system. That also indicates the rise in proficiency.

Questionnaire/Self-Assessment

Results of a brief self-assessment questionnaire are discussed in the Appendix.

Discussion

The three experiments described here were designed to study subjects’ ability to perform visual inspection (finding and counting white squares) and eye-hand coordination (placing checkers accurately on white squares) tasks. While the visual properties of these high-contrast, low-resolution tasks may seem to lend themselves particularly well for detection through a retinal prosthesis, our results show that it may take considerable practice to reach proficiency, especially for more realistic daily activities. On the other hand, a real prosthesis wearer would live with phosphene vision continuously and reduce his or her adaptation time through intensive practice. Given adequate practice, our subjects attained a level of proficiency that allowed them to complete both tasks quickly and with few errors. The initially widespread in performance levels became narrower with practice, irrespective of age and gender differences in our small subject group. Even the visually impaired subject, though not quite as proficient as the others, readily performed both tasks.

It is obvious that the simulations are idealizations of what a real

84 Walter et al.

Figure 4.7. Image series to show one of the techniques developed by the test subjects: First a white square is chosen; then the subject uses the index finger to cover the white square (depicted in images 1 to 3; movement is indicated by the red arrow); after finding the position of the chosen square the checker is placed (image 4) and its position is gradually corrected as necessary to cover the square (images 5 and 6).

have such a consistent contrast distribution as was the case in the experiments. In reality, phosphenes may be fused together, appear inhomogeneous and distorted, and there may be ongoing background noise which will reduce the visibility of the dots. None of these complexities were taken

Figure 4.8. The graphs show the times for this part of the experiment. The dashed line is the linear regression of the data.

into account during these experiments. In part, this was done because there is little knowledge about the appearance of prosthetic vision at this time. But we also kept our simulations “clean” because, contrary to a chronic implant wearer, our subjects did not continuously experience this unaccustomed visual perception, and so their visual system would not learn to interpret the incoming stimuli into meaningful data to the extent that real prosthesis wearers will.

Our results show that simple visual inspection and hand–eye coordination tasks can be accomplished with pixelized vision corresponding to a visual acuity

86 Walter et al.

Figure 4.9. These graphs show the points for each test subject.

of 20/2400. A brief questionnaire about strategies for the square counting and checker placing tasks (Table 4.1) showed that each subject developed personal ones. This suggests that there is no single best approach to solve the given tasks. Another important benefit to be gained from simulation experiments is that a skilled rehabilitation instructor can use the results and reported strategies to develop training methods for future prosthesis wearers.

The data obtained in these experiments strongly suggests that even with extremely poor vision it is possible to obtain reliable information about the environment and to react accordingly.

88 Walter et al.

Figure 4.10. Board designs used in the experiments. The white rim is not shown; it is equal in width to one square (2 cm). The black checkers used in the placing task have a diameter of 2.2 cm.

Conclusion

It is possible to perform simple tasks under pixelized conditions with very low resolution (equivalent to 20/2400 visual acuity). The results of these experiments give us confidence that a visual prosthesis with the characteristics simulated here (a 6 × 10 electrode grid with electrode-diameters of approximately 300 m and a 600 m center-to-center spacing) is likely to enable the wearer to perform similar tasks, provided such a device mediates any vision at all. A retinal prosthesis with these characteristics, while far from able to restore normal vision, may provide a solution for people who once were sighted, but lost their sight due to destruction of the outer retina. They may at least gain crude images of their environment and interpret those on the basis of their former knowledge of the visual world. With these crude images, they can attain a higher level of autonomy than they have in their present state.

Acknowledgments. This research was supported by the Landesstiftung BadenWürttemberg (MW) and R01 EY12843 (GD). The assistance of our test subjects is gratefully acknowledged.

References

1.G. Dagnelie, M. S. Humayun, R. W. Massof. Principles of Tissue Engineering, Chapter 54, pp. 761–72. Academic Press, 2nd edition, 2000.

2.Foundation Fighting Blindness. Treatments and cures; breakthroughs and beyond. http://www.blindness.org/pdfs/FFBPresentation.pdf.

3.A. Santos, M. S. Humayun, E. de Juan Jr, R. J. Greenberg, M. J. Marsh, I. B. Klock,

A.H. Milam. Preservation of the inner retina in retinitis pigmentosa. a morphometric analysis. Archives of Ophthalmology, 115:511–515, 1997.

4.R. E. Marc, B. W. Jones, C. B. Watt, E. Strettoi. Neural remodeling in retinal degeneration. Progress in Retinal and Eye Research, 22:607–55, 2003.

5.M. S. Humayun, E. de Juan Jr, G. Dagnelie, R. J. Greenberg, R. H. Propst,

D.H. Phillips. Visual perception elicited by electrical stimulation of retina in blind humans. Archives of Ophthalmology, 114:40–46, 1996.

6.J. F. Rizzo III, J. Wyatt, J. Loewenstein, S. Kelly, D. Shire. Methods and perceptual thresholds for short-term electrical stimulation of human retina with microelectrode arrays. Investigative Ophthalmology and Vision Science, 44:5355–61, 2003.

Appendix

To collect voluntary responses from subjects who were not previously familiar with the task, study participants were asked to provide feedback regarding the tests and describe their approach through a brief questionnaire administered immediately after the last session of Experiment 3. A brief summary follows.

Question 1: Eye dominance. This was important because subjects were limited to using only the left eye during the experiments, so persons with a right eye dominance (subjects 2 and 3) might have been at a disadvantage compared to those with a left eye dominance (subjects 4 and 5) and subject 1, who has no useful vision in the right eye. However, an ANOVA (SAS Institute, Research Triangle Park, NC) of test results by eye dominance shows a significant difference in errors (0.17 vs. 0.34; P < 005) and in time (97.3 vs. 82.2 s; P < 002) per trial, suggesting that the two subjects claiming left eye dominance required more time per trial, yet made fewer errors, and thus that using the dominant eye to perform this task at very poor acuity may be a mixed blessing. Note, however, that in both cases the difference is less than the standard deviation across all subjects (0.20 errors, 18.4 s), so the difference is most likely unrelated to eye dominance.

Question 2: Hand dominance. All subjects chose to use their right hand, and in fact indicated that this was their dominant hand. Therefore no conclusions regarding a possible effect of handedness can be drawn.