Literature DB >> 35017028

Test-retest reproducibility comparison in resting and the mental task states: A sensor and source-level EEG spectral analysis.

Lihong Ding1, Wei Duan1, Yulin Wang1, Xu Lei2.   

Abstract

Previous test-retest analysis of EEG mostly focused on eyes open and eyes closed resting-state. However, less attention was paid to the EEG during the subject-driven mental imaginary task state. In the current study, we compared the test-retest reproducibility of EEG spectrum in three mental imaginary task states (i.e. performed mental arithmetic, recalled the events of their day, and silently sang lyrics) and two resting states (i.e. eyes open and closed) during three EEG sessions. The resting state with eyes closed has the highest reproducibility, while the resting state with eyes opened has the lowest reproducibility for the spectral features of EEG signals at the sensor level. However, the reproducibility during eyes-open ranked higher among the five states at the source level. Moreover, the mental arithmetic state has the highest reproducibility among all the three task states. And its reproducibility in certain rhythms (theta, gamma, etc) was higher than the resting states. The reproducibility of the EEG spectrum was also investigated from the perspective of large-scale brain networks. The dorsal attention network showed the highest reproducibility in a wide frequency range of the alpha and beta rhythms. Our study suggests the importance of task selection based on the target brain region and the target frequency band. This may provide some suggestions for future researchers to choose appropriate experimental paradigms and provide a guideline on EEG study for the basic and clinical applications.
Copyright © 2022 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Electroencephalography; Mental imaginary; Power spectrum; Resting state; Test-retest analysis

Mesh:

Year:  2022        PMID: 35017028     DOI: 10.1016/j.ijpsycho.2022.01.003

Source DB:  PubMed          Journal:  Int J Psychophysiol        ISSN: 0167-8760            Impact factor:   2.997


  2 in total

1.  A Novel Approach for Segment-Length Selection Based on Stationarity to Perform Effective Connectivity Analysis Applied to Resting-State EEG Signals.

Authors:  Leonardo Góngora; Alessia Paglialonga; Alfonso Mastropietro; Giovanna Rizzo; Riccardo Barbieri
Journal:  Sensors (Basel)       Date:  2022-06-23       Impact factor: 3.847

2.  A test-retest resting, and cognitive state EEG dataset during multiple subject-driven states.

Authors:  Yulin Wang; Wei Duan; Debo Dong; Lihong Ding; Xu Lei
Journal:  Sci Data       Date:  2022-09-13       Impact factor: 8.501

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.