Electromyography (EMG) of the Extraocular Muscles (EOM)

The electrodes are sensors capable of reading EMG signals or ocular myoelectric activity during eye movements [1]. For this purpose, two vertical electrodes and two horizontal electrodes were used, with a reference electrode on the forehead (See the figure). 10 subjects performed 10 pseudo-random repetitions of each of the following eye movements during the experiment: Up, Down, Right, Left, no movement (fixation in the center) and blinking.The signal captured by the electrodes passes to an amplification stage through the AD620 or instrumentation amplifier which is a differential amplifier that eliminates much of the noise. After this stage, the signal is filtered with a pass band, which has been designed to allow the passage of signals that are in the range of frequencies of the muscular movement of the sight, which is between 0 and 40 Hz. . [two]. The implementation of low-pass and high-pass filters is carried out with a working frequency of 0.2Hz and 40Hz respectively, this creates a frequency window that allows reception and reading of the movements of the eye muscles. It is important to highlight that a conditioning circuit was implemented for vertical movement and another for horizontal movement. After conditioning, the signal goes to the ADC port of the FPGA card for its acquisition. [3] For data reading, a sampling frequency of 120 Hz was used for approximately 2 seconds, which by Nyquist's sampling theory is always 2.5 times the maximum of the signal to be acquired in this case the movement of the sight it is between 0 and 40 Hz [1]. References:[1] Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October). EMG Signal Processing with Clustering Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE.[2] Reaz, M. B. I., Hussain, M. S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification and applications. Biological procedures online, 8(1), 11-35.[3] V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020, pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.