Evidence map›Paper›PMID 38912322›Full record

ArticleCyborg and bionic systems (Washington, D.C.)2024

Neural Correlates of Motor/Tactile Imagery and Tactile Sensation in a BCI paradigm: A High-Density EEG Source Imaging Study.

Huan Wen, Yucun Zhong, Lin Yao, Yueming Wang

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Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

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0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  11. [Cross-session motor imagery-electroencephalography decoding with Riemannian spatial filtering and domain adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Huan WenThe Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Yucun ZhongThe Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Lin YaoThe Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.ORCID https://orcid.org/0000-0003-2065-7280
Yueming WangThe MOE Frontiers Science Center for Brain and Brain-Machine Integration, Zhejiang University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complementary to brain-computer interface (BCI) based on motor imagery (MI) task, sensory imagery (SI) task provides a way for BCI construction using brain activity from somatosensory cortex. The underlying neurophysiological correlation between SI and MI was unclear and difficult to measure through behavior recording. In this study, we investigated the underlying neurodynamic of motor/tactile imagery and tactile sensation tasks through a high-density electroencephalogram (EEG) recording, and EEG source imaging was used to systematically explore the cortical activation differences and correlations between the tasks. In the experiment, participants were instructed to perform the left and right hand tasks in MI paradigm, sensory stimulation (SS) paradigm and SI paradigm. The statistical results demonstrated that the imagined MI and SI tasks differed from each other within ipsilateral sensorimotor scouts, frontal and right temporal areas in α bands, whereas real SS and imagined SI showed a similar activation pattern. The similarity between SS and SI may provide a way to train the BCI system, while the difference between MI and SI may provide a way to integrate the discriminative information between them to enhance BCI performance. The combination of the tasks and its underlying neurodynamic would provide a new approach for BCI designation for a wider application. BCI studies concentrate on the hybrid decoding method combining MI or SI with SS, but the underlining neurophysiological correlates between them were unclear. MI and SI differed from each other within the ipsilateral sensorimotor cortex in alpha bands. This is a first study to investigate the neurophysiological relationship between MI and SI through an EEG source imaging approach from high-density EEG recording.

Identifiers

PMID38912322
PMCPMC11192147

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.