Consformer: Consciousness Detection Using Transformer Networks With Correntropy-Based Measures

Sun, Xuyun; Qi, Yu; Ma, Xiulin; Xu, Chuan; Luo, Benyan; Pan, Gang

doi:10.1109/tnsre.2023.3250958

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…EEG is a cost‐effective, noninvasive, portable, and bedside measurement. Many studies investigated EEG‐based biomarkers to evaluate the state of consciousness (Sun et al, 2023 ). Multiple passive paradigms like event‐related potential (Schnakers et al, 2008 ) and active paradigms like motor imagery (Cruse et al, 2011 ) have been adopted in DOC patients.…”

Section: Introductionmentioning

confidence: 99%

How well do neural signatures of resting‐state EEG detect consciousness? A large‐scale clinical study

Ma,

Qi,

et al. 2024

Human Brain Mapping

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The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large‐scale clinical resting‐state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG‐based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph‐based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index‐related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

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Section: Introductionmentioning

confidence: 99%

How well do neural signatures of resting‐state EEG detect consciousness? A large‐scale clinical study

Ma,

Qi,

et al. 2024

Human Brain Mapping

Self Cite

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State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

Wang,

Qi,

Pan

2024

Sci. China Inf. Sci.

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DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness

Zhao,

Cao,

Yang

et al. 2024

J. Neural Eng.

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Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework. Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases. Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics. Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.

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Consformer: Consciousness Detection Using Transformer Networks With Correntropy-Based Measures

Cited by 3 publications

References 44 publications

How well do neural signatures of resting‐state EEG detect consciousness? A large‐scale clinical study

How well do neural signatures of resting‐state EEG detect consciousness? A large‐scale clinical study

State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness

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