Literature DB >> 25488924

Protocol Design Challenges in the Detection of Awareness in Aware Subjects Using EEG Signals.

J Henriques1,2, D Gabriel3,4, L Grigoryeva1, E Haffen3,4,5,6, T Moulin3,4,7,8, R Aubry3,9,10, L Pazart3, J-P Ortega11,12.   

Abstract

Recent studies have evidenced serious difficulties in detecting covert awareness with electroencephalography-based techniques both in unresponsive patients and in healthy control subjects. This work reproduces the protocol design in two recent mental imagery studies with a larger group comprising 20 healthy volunteers. The main goal is assessing if modifications in the signal extraction techniques, training-testing/cross-validation routines, and hypotheses evoked in the statistical analysis, can provide solutions to the serious difficulties documented in the literature. The lack of robustness in the results advises for further search of alternative protocols more suitable for machine learning classification and of better performing signal treatment techniques. Specific recommendations are made using the findings in this work. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Entities:  

Keywords:  EEG; EEG signal classification; awareness detection; evoked potentials; mental imagery

Mesh:

Year:  2014        PMID: 25488924     DOI: 10.1177/1550059414560397

Source DB:  PubMed          Journal:  Clin EEG Neurosci        ISSN: 1550-0594            Impact factor:   1.843


  3 in total

Review 1.  Brain-Computer Interfaces for Awareness Detection, Auxiliary Diagnosis, Prognosis, and Rehabilitation in Patients with Disorders of Consciousness.

Authors:  Jiahui Pan; Jun Xiao; Jing Wang; Fei Wang; Jingcong Li; Lina Qiu; Haibo Di; Yuanqing Li
Journal:  Semin Neurol       Date:  2022-07-14       Impact factor: 3.212

2.  Neuroimaging for detecting covert awareness in patients with disorders of consciousness: reinforce the place of clinical feeling!

Authors:  Lionel Pazart; Damien Gabriel; Elodie Cretin; Regis Aubry
Journal:  Front Hum Neurosci       Date:  2015-02-17       Impact factor: 3.169

3.  Prognosis for patients with cognitive motor dissociation identified by brain-computer interface.

Authors:  Jiahui Pan; Qiuyou Xie; Pengmin Qin; Yan Chen; Yanbin He; Haiyun Huang; Fei Wang; Xiaoxiao Ni; Andrzej Cichocki; Ronghao Yu; Yuanqing Li
Journal:  Brain       Date:  2020-04-01       Impact factor: 13.501

  3 in total

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