As such, even small artifacts can reduce statistical power in a study and alter results if they happen frequently.īest efforts should always be made to prevent artifacts from entering EEG recordings. Technically speaking, artifacts add uncontrolled variability to the data, which confounds experimental observations. ![]() For example, activity from head muscles can overlap with oscillations from the brain, or movement of the cap creates distortions that affect the amplitude of an ERP. These intruding signals are known as “artifacts” and can have various physiological and non-physiological origins.Īs EEG signals typically range in low amplitudes of tens of microvolts, they can be easily blurred by artifacts, reducing the signal to noise ratio. However, intruding signals from other sources often enter the recording as well, obscuring the EEG signal of our interest. Non-invasive assessment of soft-tissue artifact and its effect on knee joint kinematics during functional activity.When recording EEG, the aim is to obtain a clear signal from the brain to help us investigate an aspect of its inner workings. Previously published items are made available in accordance with the copyright policy of the publisher. Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. The present results on soft-tissue artifact, based on fluoroscopic measurements in healthy adult subjects, may be helpful in developing location- and direction-specific weighting factors for use in global optimization algorithms aimed at minimizing the effects of soft-tissue artifact on calculations of knee joint rotations. The maximum root mean square errors for calculating knee joint rotations occurred for the open-chain knee flexion task and were 24.3 degrees, 17.8 degrees and 14.5 degrees for flexion, internal-external rotation and abduction-adduction, respectively. Markers positioned in the vicinity of the knee joint showed considerable movement, with root mean square errors as high as 29.3mm. Soft-tissue artifact for the thigh markers was substantially greater than that for the shank markers. Although a consistent pattern of soft-tissue artifact was found for each task across all subjects, the magnitudes of soft-tissue artifact were subject-, task- and location-dependent. A number of different skin-marker clusters (total of 180) were used to calculate knee joint rotations, and the results were compared against those obtained from fluoroscopy. ![]() Soft-tissue artifact was defined as the degree of movement of each marker in the anteroposterior, proximodistal and mediolateral directions of the corresponding anatomical frame. Knee joint kinematics was derived using the anatomical frames from the MRI-based, 3D bone models together with the data from video motion capture and X-ray fluoroscopy. Subject-specific bone models generated from magnetic resonance imaging (MRI) were used in conjunction with X-ray images obtained from single-plane fluoroscopy to determine three-dimensional knee joint kinematics for four separate tasks: open-chain knee flexion, hip axial rotation, level walking, and a step-up. The aim of this study was twofold: first, to quantify lower limb soft-tissue artifact in young healthy subjects during functional activity and second, to determine the effect of soft-tissue artifact on the calculation of knee joint kinematics. The soft-tissue interface between skin-mounted markers and the underlying bones poses a major limitation to accurate, non-invasive measurement of joint kinematics.
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