人類的眼球運動是經由中樞神經系統，包括大腦、小腦、腦幹等相互配合而完成，因此對於中樞神經系統受損或病變、退化的患者，其所產生的眼球運動將異於正常人，帕金森氏症的患者即為一例。過去的文獻指出，跳視眼球運動的驅動訊號主要是由脈衝(pulse)與步階(step)二個成份所組成，其中脈衝成份讓眼球能夠快速移動到目標點，而步階成份則是讓眼球能夠持續凝視在目標上。當某些神經元發生病變時，會導致脈衝成份或步階成份的異常。脈衝成份發生異常時，會影響跳視運動的峰值速度、動作時間及最大超越量（overshoot）；步階成份異常時，患者往往無法凝視目標點而會降低跳視運動的精確度，同時也會影響跳視的峰值速度與動作時間。近年來獨立成份分析(ICA)技術被廣泛應用於分離訊號中的隱藏成份，因此本研究的第一個目的為藉由獨立成份分析技術分離隱藏在跳視眼球運動的脈衝與步階成份。
以眼電圖記錄眼球運動往往會因雜訊過大而降低解析度。另外，利用眼電圖所量測之外旋跳視比內轉跳視之峰值速度低，產生和搜尋線圈完全相反之量測結果。因此，本研究的第二個目的為建構一套精密的紅外線眼球運動量測系統，設計相關的工具軟體以利後續的研究。擷取的跳視軌跡經由時域及頻域分析後的結果和文獻資料作比較，以確定系統的精確性。
本研究共邀請四位受測者（二位男性及二位女性，年齡為24至26歲之間），藉由紅外線眼球運動量測儀器擷取受測者的跳視軌跡。結果顯示，脈衝成份確實主導整個跳視運動的進行，當跳視振幅逐漸加大時，對應的脈衝成份也隨之增大。根據跳視振幅為15度的分析結果顯示，脈衝成份到達峰值的時間略慢於峰值速度發生的時間約1至2微秒之間，頻譜分析的結果顯示，當跳視振幅加大時，其頻譜的區域最小值會往低頻方向移動，表示跳視振幅變大時所需的動作時間加長，訊號逐漸偏向低頻。能量頻譜上顯示，發生最小能量（M1、M2與M3）處之頻率的倒數和動作區間呈線性關係；另外M2 與M3並不與M1呈諧波關係，由此可以推論產生跳視的脈衝並不是一個矩形波。以上頻譜分析的結果與文獻的結果符合，這意指採用紅外線眼球運動量測儀器確實可以進行高精確度的量測。
未來將利用ICA分析病患之跳視軌跡，探討神經病變對脈衝成分與步階成份所造成的影響。

The activation and control of eye movements is a complicated process in the central nervous system (CNS), which is proceeded through the coordination of cerebrum, cerebellum, brain stem, and other parts of the brain. For patients suffering from CNS lesions, the pathological changes or degeneration will produce eye movement disorders. Parkinson’s disease is an example. Previous studies showed that the driving signals of saccade generation consist of pulse and step. The pulse component moves the eye to the target position, while the step component makes the eye fixate at the target position. The pulse and step will be abnormal if the neurons responsible for the generation of saccades are degenerated or disordered. If the pulse is not normal, the peak velocity, duration, and saccade overshoot will be affected, while the abnormal step will decrease the accuracy of the saccades since the patients are unable to fixate at the target. Recently the technique of independent component analysis (ICA) has been proposed to separate the underlying components in the signal set. The first goal of this study used ICA to separate the pulse and step components in the recorded saccade profiles.
Recording the trajectories of eye movements by Electro-oculogram (EOG) embeds more noise than other techniques, which causes the resolution to be greatly decreased. In addition, the abductive saccades present lower peak velocity than the adductive, which is against the previous investigation and might be caused by EOG artifacts. Therefore, the second goal was to set up an infrared eye tracker system with great precision for eye movement recording, accompanied programs were also designed for target control, target pattern generation, and data analyses. The results of temporal and spectral analyses of the recorded saccadic profiles were compared with the results obtained by previous investigations, which is useful for ascertaining the precision of the recording device.
Four subjects (two males and two females) with age ranging from 24 to 26 were recruited for recording saccades by infrared eye tracker in the experiment. The results show that the pulse component dominates the progression of saccades, the pulse amplitude increases with the saccades. The time when the pulse arrived at its maximum intensity was delay by 1 to 2 ms if compared with the time that the peak velocity occurred in the saccadic profile. The results of spectral analysis demonstrates that the local minimum of the power spectrum shifts to lower frequency when the saccade amplitude increases, which indicates that the duration increases with saccades amplitude. Also the reciprocals of the frequencies corresponding to each local minimum were proportional to the durations of saccade. The frequencies corresponding to each local minimum do not follow harmonic relationship, which means that the pulse component of a saccade is actually not a square wave as suggested by some models. The results of spectral analysis match with previous studies, this shows that the infrared eye tracker is capable of recording eye movement profile with great accuracy.
Future works will be emphasized on the decomposition of the pulse and the step components of saccades and study their correlation with the neurological disease for the patients.

1. Bahill AT, Clark MR, Stark L. The main sequence, a tool for studying human eye movements. Math Biosci 24:191-204, 1975.

2. Baloh RW, Sills AW, Kumley WE, Honrubia V. Quantitative measurement of saccade amplitude, duration, and velocity. Neurology 25:1065-1070, 1975.

3. Baloh RW, Konrad HR, Sills AW, Honrubia WE. The saccade velocity test. Neurology 25:1071-1076, 1975.

4. Becker W, Jurgen R. An analysis of the saccadic system by means of double step stimuli. Vision Res 19:967-983, 1979.

5. Chen T, Chen YF, Lin CH, Tsai TT. Quantification analysis for saccadic eye movements. Ann Biom Eng 26:1065-1071, 1998.

6. Chen Y-F, Chen T, Tsai T-T. Analysis of volition latency on antisaccadic eye movements. Med Eng Phy 21:555-562, 1999.

7. Chen Y-F, Chen T, Tsai T-T, Huang H-H. Performance analysis of visual fixation. Biomed Eng Appl Basis Comm 11:121-127, 1999,

8. Chen Y-F, Lin H-H, Chen T, Tsai T-T, Shih I-F. The peak velocity and skewness relationship for the reflexive saccades. Biomed Eng Appl Basis Comm 2002a; 14 (in press).

9. Chen Y-F, Quantitative analysis of saccadic eye movement and its clinical applications. PHD thesis, NCKU 2002b.

10. Ciuffreda KJ, Tannen B. Eye movement basics for the clinicians , 1995.

11. Collewijn H, Erkelens CJ, Steinman RM. Binocular coordination of human horizontal saccadic eye movements. J Physiol (London) 404:157-182, 1988.

12. Gibson JM, Pimlott R, Kennard C. Ocular motor and manual tracking in Parkinson’s disease and the effect of treatment. J Neurol Neurosur Psych 50:853-860, 1987.

13. Harris CM, Wallman J, Scudder CA. Fourier analysis of saccades in monkeys and humans. J Neurophysiol 63:877-886, 1990.

14. Harwood MR, Mezey LE, Harris CM. The spectral main sequence of human saccades. J Neurosci 19:9098-9106, 1999.

15. Hikosaka O, Sakamoto M, Usui S. Functional properties of monkey caudate neurons. I. Activities related to saccadic eye movements. J Neurophysiol 61:780-798, 1989.

16. Hyvärinen A, Karhunen Juha, Oja E. Independent Component Analysis 1st ed. 2000.

17. Jürgens R, Becker W, and Kornhuber HH. Natural and drug-induced variations of velocity and duration of human saccadic eye movements: evidence for a control of the neural pulse generator by local feedback. Biol Cybern 39:87-96, 1981.

18. Leigh RJ, Zee DS. The neurology of eye movements. 3rd ed., 1999.

19. Rascol O, Clanet M, Montastruc JL, Simonetta M, Soulier-Esteve MJ, Doyon B, Rascol A. Abnormal ocular movements in Parkinson’s disease. Brain 112: 1193-1214, 1989.

20. Richard S. Introduction to Applied Statical Signal Analysis, 2nd ed., 1999.

21. Robinson DA. Oculomotor unit behavior in the monkey. J Neurophysiol 33:393-404, 1970.

22. Semmlow J. L., Yuan W., “Component of Disparity Eye Movements: Application of Independent Component Analysis,” IEEE Trans. Biomed. Eng. 49:805-811, 2002.

23. Smit AC, Van Gisbergen, Cools AR. A parametric analysis of human saccades in different experimental paradigms. Vision Res 27, 10:1745-1762, 1987.

24. Takagi M, Abe H, Hasegawa S, Yoshizawa T, Usui T. Velocity profile of human horizontal saccades. Ophthalmologica 206:169-176, 1993.

25. Van Gisbergen JAM, Robinson DA, Gielen S. A quantitative analysis of generation of saccadic eye movements by burst neurons. J Neurophysiol 45:417-442, 1981.

26. Van Opstal AJ, Van Gisbergen JAM, Eggermont JJ. Reconstruction of neural control signals for saccades based on an inverse method. Vision Res 25:789-801, 1985.

27. Van Opstal AJ, Van Gisbergen JAM. Skewness of saccadic velocity profiles: a unifying parameter for normal and slow saccades. Vision Res 27, 5:731-745, 1987.