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].The EMG signals were recorded with a data acquisition equipment with a resolution of 10 bits, that is the reason why the data is in the range of 0 - 1024. 1024 being five volts of direct current. The EMG signals were recorded with a data acquisition equipment with a resolution of 10 bits, that is the reason why the data is in the range of 0 - 1024. 1024 being five volts of direct current. ⭐ When using this resource, please cite the original publication:V. Asanza et al., "Electrooculography Signals Classification for FPGA-based Human-Computer Interaction," 2022 IEEE ANDESCON, Barranquilla, Colombia, 2022, pp. 1-7, doi: 10.1109/ANDESCON56260.2022.9989664.GitHub:https://github.com/vasanza/Matlab_CodeRead related topics:https://vasanza.blogspot.com/References: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). IEEEReaz, 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), 11-35V. 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/ICEDEG48599.2020.9096752C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817

电极作为能够读取肌电图信号或眼动肌电活动的传感器[1]。为此，实验中采用了两个垂直电极和两个水平电极，并在额头位置设置了一个参考电极（见图）。10名受试者在实验过程中分别对以下眼动进行了10次伪随机重复：向上、向下、向右、向左、无运动（中心注视）和眨眼。电极捕获的信号通过AD620或仪器放大器传输至放大阶段，该放大器为差分放大器，能有效消除大部分噪声。在此阶段之后，信号经过一个通带滤波器，该滤波器经过精心设计，以允许在视觉肌肉运动频率范围内（介于0至40赫兹之间）的信号通过。[two]。低通和高通滤波器的实施分别以0.2赫兹和40赫兹的工作频率进行，从而创建了一个频率窗口，允许接收和读取眼肌的运动。需特别强调的是，为垂直运动和水平运动分别实施了一个条件电路。条件处理后，信号传输至FPGA卡的ADC端口进行采集。[3]数据读取时，使用120赫兹的采样频率，持续约2秒，根据奈奎斯特采样理论，这始终是所采集信号最大值的2.5倍，在本例中，即视觉运动介于0至40赫兹[1]。肌电图信号使用具有10位分辨率的采集设备记录，这也是数据范围在0至1024的原因，其中1024对应直流五伏。[two]。当使用此资源时，请引用原始出版物：V. Asanza等人，《基于FPGA的人机交互中的眼电图信号分类》，2022年IEEE ANDESCON，哥伦比亚巴兰基亚，2022年，第1-7页，doi：10.1109/ANDESCON56260.2022.9989664.GitHub：https://github.com/vasanza/Matlab_Code。阅读相关主题：https://vasanza.blogspot.com/参考文献：Asanza, V.，Peláez, E.，Loayza, F.，Mesa, I.，Díaz, J.，& Valarezo, E.（2018年10月）。《基于聚类算法的运动手势任务的肌电图信号处理》，2018年厄瓜多尔第三次技术章节会议（ETCM），第1-6页。Reaz, M. B. I.，Hussain, M. S.，& Mohd-Yasin, F.（2006年）。肌电图信号分析技术：检测、处理、分类及其应用。生物过程在线，8（1），11-35。V. Asanza，A. Constantine，S. Valarezo和E. Peláez，《基于FPGA的脑电图信号分类系统》，2020年第七届国际电子民主与电子政府会议（ICEDEG），阿根廷布宜诺斯艾利斯，2020年，第87-92页，doi：10.1109/ICEDEG48599.2020.9096752。C. Cedeño Z.，J. Cordova-Garcia，V. Asanza A.，R. Ponguillo和L. Muñoz M.，《基于k-NN的EMG识别用于具有有限硬件资源的手势通信》，2019年IEEE SmartWorld，智能计算，高级与可信计算，可扩展计算与通信，云计算与大数据计算，物联网与智能城市创新（SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI），英国莱斯特，2019年，第812-817页。