Emg Signal Acquisition
Emg Signal Acquisition - Related Videos
07:16
Extraction of the EPP Component from the Surface EMG
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Cited by 2 •
2009
The endplate potential (EPP) component can be extracted from the surface EMG using a digital filter. The extracted EPP shows oscillation with a frequency of about 30 Hz.
11:17
Acquisition and Analysis of an ECG (electrocardiography) Signal
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2023
Source: Peiman Shahbeigi-Roodposhti and Sina Shahbazmohamadi, Biomedical Engineering Department, University of Connecticut, Storrs, Connecticut An electrocardiograph is a graph recorded by electric potential changes occurring between electrodes placed on a patient's torso to demonstrate cardiac activity. An ECG signal tracks heart rhythm and many cardiac diseases, such as poor blood flow to the heart and structural abnormalities. The action potential created by contractions of the heart wall...
11:31
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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Cited by 16 •
2014
Monitoring brain activity during upright motor tasks is of great value when investigating the neural source of movement disorders. Here, we demonstrate a protocol that combines functional near infrared spectroscopy with continuous monitoring of muscle and kinematic activity during 4 types of motor tasks.
12:39
High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
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Cited by 24 •
2016
Instructions for the low-cost construction and surgical implantation of a chronic transcranial high-density electroencephalographic montage into mice are provided. Signal recording, extraction, and processing techniques are also described.
11:25
Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Cited by 28 •
2013
Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.
FAQs
The main distinction is where the electrodes detect muscle-generated voltage changes: surface electrodes measure from outside the muscle, whereas intramuscular electrodes are used within muscle tissue. Both target electrical activity associated with motor unit action potentials, but they represent different acquisition arrangements researchers can select when studying neuromuscular function.
Amplification makes detected voltage changes more suitable for recording, while filtering reduces noise that could obscure the muscle signal. These processing steps work together to preserve relevant signal features and improve the usefulness of the acquired recording for evaluating neuromuscular activity, rather than treating every detected electrical fluctuation as meaningful.
Appropriate sampling helps preserve the signal features that researchers need to analyze. If acquisition does not sample suitably, important characteristics of the recorded electrical activity may not be retained. Sampling therefore complements electrode detection, amplification, and filtering by helping maintain the information needed to study muscle activity and motor control.
A basic workflow begins by selecting surface or intramuscular electrodes and using them to detect voltage changes generated by motor unit action potentials. The detected activity is then amplified, filtered, and sampled appropriately so relevant features remain available for analysis. This sequence produces recordings that can support neuromuscular assessment.
In neuroscience, these recordings provide information about motor control, muscle coordination, and communication between the nervous system and muscles. Researchers can examine the acquired activity while studying how muscles participate in movement and how neuromuscular function changes. This makes the technique useful for connecting electrical muscle activity with broader nervous-system processes.
The method supports studies of movement and neuromuscular disorders, where acquired signals can contribute to evaluating neuromuscular function. It also has applications in rehabilitation, prosthetic control, and human-machine interfaces. Across these settings, recordings provide muscle-activity information that can inform research or support interaction between muscular activity and engineered systems.