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Using a motion capture system for spatial localization of EEG electrodes

Overview of attention for article published in Frontiers in Neuroscience, April 2015
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Title
Using a motion capture system for spatial localization of EEG electrodes
Published in
Frontiers in Neuroscience, April 2015
DOI 10.3389/fnins.2015.00130
Pubmed ID
Authors

Pedro M. R. Reis, Matthias Lochmann

Abstract

Electroencephalography (EEG) is often used in source analysis studies, in which the locations of cortex regions responsible for a signal are determined. For this to be possible, accurate positions of the electrodes at the scalp surface must be determined, otherwise errors in the source estimation will occur. Today, several methods for acquiring these positions exist but they are often not satisfyingly accurate or take a long time to perform. Therefore, in this paper we describe a method capable of determining the positions accurately and fast. This method uses an infrared light motion capture system (IR-MOCAP) with 8 cameras arranged around a human participant. It acquires 3D coordinates of each electrode and automatically labels them. Each electrode has a small reflector on top of it thus allowing its detection by the cameras. We tested the accuracy of the presented method by acquiring the electrodes positions on a rigid sphere model and comparing these with measurements from computer tomography (CT). The average Euclidean distance between the sphere model CT measurements and the presented method was 1.23 mm with an average standard deviation of 0.51 mm. We also tested the method with a human participant. The measurement was quickly performed and all positions were captured. These results tell that, with this method, it is possible to acquire electrode positions with minimal error and little time effort for the study participants and investigators.

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Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Canada 1 2%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 12 27%
Student > Ph. D. Student 8 18%
Student > Bachelor 5 11%
Researcher 3 7%
Professor > Associate Professor 2 5%
Other 5 11%
Unknown 9 20%
Readers by discipline Count As %
Medicine and Dentistry 8 18%
Engineering 6 14%
Agricultural and Biological Sciences 4 9%
Neuroscience 4 9%
Psychology 3 7%
Other 7 16%
Unknown 12 27%