TY  - JOUR
AU  - Bukhari, W.M. 
AU  - Daud, W. 
AU  - Sudirman, Rubita 
AU  - Omar, Camallil 
PY  - 2011
TI  - Identification of Electrooculography Signals Frequency Energy Distribution Using Wavelet Algorithm
JF  - Journal of Computer Science
VL  - 7
IS  - 11
DO  - 10.3844/jcssp.2011.1619.1625
UR  - https://thescipub.com/abstract/jcssp.2011.1619.1625
AB  - Problem statement: The time frequency analysis of non-stationary signals has been the
considerable research effort in recent years. Wavelet transform is one of the favored tool for the
analyzing the biomedical signals. Approach: We describe the identification of Electro-Oculograph (EOG) signals of eye movement potentials by using wavelet algorithm which gives a
lot of information than FFT. The capability of wavelet transform was to distribute the signal
energy with the change of time in different frequency bands. This will showed the characteristic of
the signals since energy was an important physical variable in signal analysis. The EOG signals
were captured using electrodes placed on the forehead around the eyes to record the eye
movements. The wavelet features used to determine the characteristic of eye movement waveform.
This technique adopted because it was a non-invasive, inexpensive and accurate. The new
technology enhancement has allowed the EOG signals captured using the Neuronal EEG-9200.
The recorded data was composed of an eye movement toward four directions, i.e., downward,
upward, leftward and rightward. The proposed analysis for each eyes signal is analyzed by using
Wavelet Transform (WT) with energy algorithm and by comparing the energy distribution with
the change of time and frequency of each signal. Results: A wavelet Scalogram was plotted to
display the different percentages of energy for each wavelet coefficient towards different
movement. Conclusion: From the result, it is proved that the different EOG signals exhibit
differences in signals energy with their corresponding scale such as leftward with scale 6 (8-
16Hz), rightward with scale 8 (2-4Hz), downward with scale 9 (1-2Hz) and upward with level 7
(4-8Hz). Statistically, the results in this study indicate that there are 93% (averages) significance
differences in the extracted features of wavelet Scalogram analysis.