Consformer: consciousness detection using transformer networks with correntropy-based measures

Consciousness detection is important in diagnosis and treatment of disorders of consciousness (DOC). Recent studies have demonstrated that electroencephalography (EEG) signals contain effective information for consciousness state evaluation. We propose two novel EEG measures: the spatiotemporal corr...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering 2023-01, Vol.31, p.1-1
Main Authors: Sun, Xuyun, Qi, Yu, Ma, Xiulin, Xu, Chuan, Luo, Benyan, Pan, Gang
Format: Article
Language:English
Subjects:
Brain
Complexity
Complexity theory
Consciousness
Consciousness detection
correntropy
EEG
electroencephalogram
Electroencephalography
Humans
Learning
machine learning
Neural networks
Neuromodulation
Optimization
Persistent Vegetative State - diagnosis
Probability density function
Recording
Spatiotemporal phenomena
transformer
Transformers
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Summary:Consciousness detection is important in diagnosis and treatment of disorders of consciousness (DOC). Recent studies have demonstrated that electroencephalography (EEG) signals contain effective information for consciousness state evaluation. We propose two novel EEG measures: the spatiotemporal correntropy and the neuromodulation intensity, to reflect the temporal-spatial complexity in brain signals for consciousness detection. Then, we build a pool of EEG measures with different spectral, complexity and connectivity features, and propose Consformer, a transformer network to learn an adaptive optimization of features for different subjects with the attention mechanism. Experiments are carried out using a large dataset of 280 resting-state EEG recordings of DOC patients. Consformer discriminates minimally conscious state (MCS) from vegetative state (VS) with an accuracy of 85.73% and an F1-score of 86.95%, which achieves the state-of-the-art performance.
ISSN:1534-4320
1558-0210
DOI:10.1109/TNSRE.2023.3250958