Literature DB >> 15604010

Technical quality evaluation of EEG recording based on electroencephalographers' knowledge.

Masatoshi Nakamura1, Qian Chen, Takenao Sugi, Akio Ikeda, Hiroshi Shibasaki.   

Abstract

The aim of this study is to develop a technical quality evaluation system of electroencephalogram (EEG) recording in order to acquire technically satisfactory EEG records, which may contribute to the accuracy improvement of EEG interpretation. In our developed system, the evaluation of EEG recording comprises the detection of technical artifacts and physiological status, which indicates the recording status objectively. In addition, the caution signals to users are generated in the system according to the undesired status detected. The information displayed to users includes the updated EEG records and instant evaluation results. Two examples of evaluation results are introduced in this paper, illustrating unsatisfactory records and artifact free records, respectively. The experimental results are proposed to verify the effectiveness of the technical quality evaluation of EEG recording. The implementation of the technical quality evaluation of EEG recording is helpful to acquire technically satisfactory EEG records, which may improve the accuracy of results in both the visual and the automatic EEG interpretation, and ease the laborious work of EEG technicians in the recording progress.

Mesh:

Year:  2005        PMID: 15604010     DOI: 10.1016/j.medengphy.2004.09.001

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  4 in total

1.  Automatic interpretation of hyperventilation-induced electroencephalogram constructed in the way of qualified electroencephalographer's visual inspection.

Authors:  Xiu Zhang; Xingyu Wang; Takenao Sugi; Akio Ikeda; Takashi Nagamine; Hiroshi Shibasaki; Masatoshi Nakamura
Journal:  Med Biol Eng Comput       Date:  2010-10-12       Impact factor: 2.602

2.  Acquisition technology research of EEG and related physiological signals under +Gz acceleration.

Authors:  Y Li; T Zhang; L Deng; B Wang
Journal:  Ir J Med Sci       Date:  2013-07-17       Impact factor: 1.568

3.  A Motor Imagery Signals Classification Method via the Difference of EEG Signals Between Left and Right Hemispheric Electrodes.

Authors:  Xiangmin Lun; Jianwei Liu; Yifei Zhang; Ziqian Hao; Yimin Hou
Journal:  Front Neurosci       Date:  2022-05-09       Impact factor: 5.152

4.  Quality Assessment of Single-Channel EEG for Wearable Devices.

Authors:  Fanny Grosselin; Xavier Navarro-Sune; Alessia Vozzi; Katerina Pandremmenou; Fabrizio De Vico Fallani; Yohan Attal; Mario Chavez
Journal:  Sensors (Basel)       Date:  2019-01-31       Impact factor: 3.576

  4 in total

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