Literature DB >> 20186487

Single camera photogrammetry system for EEG electrode identification and localization.

Uğur Baysal1, Gökhan Sengül.   

Abstract

In this study, photogrammetric coordinate measurement and color-based identification of EEG electrode positions on the human head are simultaneously implemented. A rotating, 2MP digital camera about 20 cm above the subject's head is used and the images are acquired at predefined stop points separated azimuthally at equal angular displacements. In order to realize full automation, the electrodes have been labeled by colored circular markers and an electrode recognition algorithm has been developed. The proposed method has been tested by using a plastic head phantom carrying 25 electrode markers. Electrode locations have been determined while incorporating three different methods: (i) the proposed photogrammetric method, (ii) conventional 3D radiofrequency (RF) digitizer, and (iii) coordinate measurement machine having about 6.5 mum accuracy. It is found that the proposed system automatically identifies electrodes and localizes them with a maximum error of 0.77 mm. It is suggested that this method may be used in EEG source localization applications in the human brain.

Entities:  

Mesh:

Year:  2010        PMID: 20186487     DOI: 10.1007/s10439-010-9950-4

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  9 in total

1.  Smartphone-based photogrammetry provides improved localization and registration of scalp-mounted neuroimaging sensors.

Authors:  Ilaria Mazzonetto; Marco Castellaro; Robert J Cooper; Sabrina Brigadoi
Journal:  Sci Rep       Date:  2022-06-27       Impact factor: 4.996

2.  Accuracy of high-density EEG electrode position measurement using an optical scanner compared with the photogrammetry method.

Authors:  Orsolya Györfi; Cheng-Teng Ip; Anders Bach Justesen; Maria Louise Gam-Jensen; Connie Rømer; Martin Fabricius; Lars H Pinborg; Sándor Beniczky
Journal:  Clin Neurophysiol Pract       Date:  2022-05-02

3.  Guidelines and best practices for electrophysiological data collection, analysis and reporting in autism.

Authors:  Sara Jane Webb; Raphael Bernier; Heather A Henderson; Mark H Johnson; Emily J H Jones; Matthew D Lerner; James C McPartland; Charles A Nelson; Donald C Rojas; Jeanne Townsend; Marissa Westerfield
Journal:  J Autism Dev Disord       Date:  2015-02

4.  Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

Authors:  Tommy Clausner; Sarang S Dalal; Maité Crespo-García
Journal:  Front Neurosci       Date:  2017-05-16       Impact factor: 4.677

5.  Optical Co-registration of MRI and On-scalp MEG.

Authors:  Rasmus Zetter; Joonas Iivanainen; Lauri Parkkonen
Journal:  Sci Rep       Date:  2019-04-02       Impact factor: 4.379

6.  Automated 3D thorax model generation using handheld video-footage.

Authors:  Nadine Dussel; Reinhard Fuchs; Andreas W Reske; Thomas Neumuth
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-03-31       Impact factor: 3.421

7.  Multimodal spatial calibration for accurately registering EEG sensor positions.

Authors:  Jianhua Zhang; Jian Chen; Shengyong Chen; Gang Xiao; Xiaoli Li
Journal:  Comput Math Methods Med       Date:  2014-04-03       Impact factor: 2.238

8.  Requirements for Coregistration Accuracy in On-Scalp MEG.

Authors:  Rasmus Zetter; Joonas Iivanainen; Matti Stenroos; Lauri Parkkonen
Journal:  Brain Topogr       Date:  2018-06-22       Impact factor: 3.020

9.  More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, but Current Methods Already Accurately Identify Brodmann Areas.

Authors:  Seyed Yahya Shirazi; Helen J Huang
Journal:  Front Neurosci       Date:  2019-11-06       Impact factor: 4.677

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.