Integrating task-state electroencephalography and motor function for intelligent assessment of recovery status from prolonged disorders of consciousness.
4 affiliations
- 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
- 2School of Public Health, Peking University, Beijing, 100191, China. [email protected].
- 3Key Laboratory of Health System Reform and Governance, National Health Commission of China (Peking University), Beijing, 100191, China. [email protected].
- 4Beijing Institute for Health Development, Peking University, Beijing, 100191, China. [email protected].
Abstract
Background
Accurately assessing recovery in patients with prolonged disorders of consciousness (PDoC) is essential for evaluating the therapeutic efficacy and optimizing clinical care. However, substantial individual variability and poorly understood recovery mechanisms render this task exceptionally challenging.Methods
We collected task-state electroencephalography and clinical records from 78 PDoC patients to develop an intelligent assessment framework. Power spectral densities (PSDs) and coherence values (CVs) derived from event-related potentials (ERPs) were extracted and integrated with motor function scores to construct a multidimensional feature set. Linear support vector machine (SVM) and logistic regression (LR) models were built and rigorously evaluated via leave-one-out cross-validation (LOOCV). SHapley Additive exPlanations (SHAP) was utilized for visual explanation.Results
The LOOCV indicated that both LR and SVM achieved optimal, similar performance in integrating alpha-band features with motor function (Macro-Average F1-score [Ma-F]: 76.79% versus 75.54%). The Receiver Operating Characteristic curves (with Area Under the Curve values of 77.57% for LR and 72.82% for SVM) and Precision-Recall curves (with Average Precision values of 68.87% for LR and 59.85% for SVM) suggested that the framework performs stably. The reliability curves indicated that both models demonstrate good calibration and are reliable for assessment.Conclusions
The study provides preliminary evidence that alpha-band PSDs and CVs derived from ERPs potentially enhance the motor function assessment performance. This integrated paradigm provides a valuable indicator. Overall, our findings suggest that combining neuroscience with intelligent technology holds promise for improving personalized diagnosis and optimizing the PDoC management.
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BibTeX
@article{zhang2026integrating,
title = {{Integrating task-state electroencephalography and motor function for intelligent assessment of recovery status from prolonged disorders of consciousness.}},
author = {Longhao Zhang and Hongzhen Cui and Xiaoyue Zhu and Guilong Zhang and Yunfeng Peng},
journal = {BMC neurology},
year = {2026},
month = jul,
doi = {10.1186/s12883-026-05183-y},
url = {https://europepmc.org/article/MED/42902244},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Integrating task-state electroencephalography and motor function for intelligent assessment of recovery status from prolonged disorders of consciousness.
AU - Longhao Zhang
AU - Hongzhen Cui
AU - Xiaoyue Zhu
AU - Guilong Zhang
AU - Yunfeng Peng
PY - 2026
DA - 2026/07/01
JO - BMC neurology
DO - 10.1186/s12883-026-05183-y
UR - https://europepmc.org/article/MED/42902244
AB - Background Accurately assessing recovery in patients with prolonged disorders of consciousness (PDoC) is essential for evaluating the therapeutic efficacy and optimizing clinical care. However, substantial individual variability and poorly understood recovery mechanisms render this task exceptionally challenging.Methods We collected task-state electroencephalography and clinical records from 78 PDoC patients to develop an intelligent assessment framework. Power spectral densities (PSDs) and coherence values (CVs) derived from event-related potentials (ERPs) were extracted and integrated with motor function scores to construct a multidimensional feature set. Linear support vector machine (SVM) and logistic regression (LR) models were built and rigorously evaluated via leave-one-out cross-validation (LOOCV). SHapley Additive exPlanations (SHAP) was utilized for visual explanation.Results The LOOCV indicated that both LR and SVM achieved optimal, similar performance in integrating alpha-band features with motor function (Macro-Average F1-score [Ma-F]: 76.79% versus 75.54%). The Receiver Operating Characteristic curves (with Area Under the Curve values of 77.57% for LR and 72.82% for SVM) and Precision-Recall curves (with Average Precision values of 68.87% for LR and 59.85% for SVM) suggested that the framework performs stably. The reliability curves indicated that both models demonstrate good calibration and are reliable for assessment.Conclusions The study provides preliminary evidence that alpha-band PSDs and CVs derived from ERPs potentially enhance the motor function assessment performance. This integrated paradigm provides a valuable indicator. Overall, our findings suggest that combining neuroscience with intelligent technology holds promise for improving personalized diagnosis and optimizing the PDoC management.
SN - 1471-2377
ER -