1983
DOI: 10.1109/tbme.1983.325108
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Frequency Limitations and Optimal Step Size for the Two-Point Central Difference Derivative Algorithm with Applications to Human Eye Movement Data

Abstract: Abstract-There are many algorithms for calculating derivatives. The two-point central difference algorithm is the simplest. Besides simplicity, the two most important characteristics of this algorithm are accuracy and frequency response. The frequency content of the data prescribes a lower limit on the sampling rate. The smoothness and accuracy of the data determine the optimal step size. We discuss the low-pass filter characteristics of this algorithm and derive the optimal step size for two types of human ey… Show more

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Cited by 103 publications
(49 citation statements)
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References 5 publications
(9 reference statements)
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“…During the experiment, eye velocity was extracted on-line from the position signal, using a two-point central difference derivative algorithm (Bahill and McDonald, 1983). The change in target location occurred, in the jump conditions, when eye velocity reached a level roughly equal to half of its the peak value.…”
Section: Experimental Conditionsmentioning
confidence: 99%
“…During the experiment, eye velocity was extracted on-line from the position signal, using a two-point central difference derivative algorithm (Bahill and McDonald, 1983). The change in target location occurred, in the jump conditions, when eye velocity reached a level roughly equal to half of its the peak value.…”
Section: Experimental Conditionsmentioning
confidence: 99%
“…Eye position was digitized at 500 samples/sec. A five-point central difference algorithm (Bahill and McDonald 1983) was used to derive velocity from eye position.…”
Section: Eye Movement Apparatus and Protocolmentioning
confidence: 99%
“…We used a two-point central difference algorithm to compute smooth velocity as a function of time (Bahill et al 1982;Bahill and McDonald 1983a). A step size of 27 ms was used thus producing a bandwidth of 8.2 Hz.…”
Section: Digital Computation Of Eye Velocitymentioning
confidence: 99%