讲师(高校)
性别:男
出生日期:1987-04-06
毕业院校:The University of Tokyo
学历:博士研究生
学位:工学博士学位
入职时间:2017-09-19
所在单位:华侨大学信息学院
办公地点:华侨大学厦门校区机电信息大楼B544
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在职信息:在岗
职务:讲师
学科:模式识别与智能系统
信号与信息处理生物医学工程生物医学工程 学术荣誉:2018 当选:福建省引进高层次人才
2023-09-30 厦门市高层次人才B类
2023-09-30 福建省高层次人才B类
2022-09-30 厦门市高层次人才C类
2022-09-30 福建省高层次人才C类
2019-04-30 泉州市人才
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发表刊物:Frontiers in Neurology
摘要:Introduction: Accurate assessment of disorders of consciousness (DoC), including minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS/VS), remains a major challenge in clinical neurorehabilitation. EEG-based biomarkers offer a non-invasive approach for characterizing residual brain activity, yet their discriminative sensitivity may depend on auditory stimulus design. Methods: EEG recordings were obtained from 15 male patients with DoC during four auditory stimulation paradigms. Nonlinear, spectral power, and graph-theoretic features were extracted, yielding 136 candidate features. All individual features were independently evaluated using patient-level leave-one-out cross-validation (LOOCV) to characterize paradigm- and feature-domain-specific predictive performance. In a complementary exploratory optimization analysis, univariate statistical screening identified 26 features with nominal group differences (p < 0.05); these features were subsequently subjected to within-fold K-means clustering and principal component analysis (PCA), and compared with directly combined feature representations for MCS versus UWS/VS classification and CRS-R score regression. Results: In the single-feature benchmark, the reversed-word sequence paradigm showed relatively higher averaged classification performance, while nonlinear features demonstrated comparatively stronger classification characteristics than spectral power and graph-theoretic features. In the optimization analysis, the PCA-derived representation achieved competitive classification performance, whereas the direct combination of all statistically screened features achieved the highest observed classification accuracy. Regression performance was generally limited. Discussion: These findings suggest that auditory stimulus design and EEG feature representation may influence the discrimination of consciousness states in DoC. In this small-sample exploratory study, reversed-word auditory stimulation and nonlinear EEG features showed promising discriminative characteristics, while direct feature combination provided additional predictive information for categorical classification. Further validation in larger and independent cohorts is required.
论文类型:期刊论文
是否译文:否
发表时间:2026-09-30
收录刊物:SCI
DOI码:10.3389/fneur.2026.1927313
第一作者:段放,陈建铭
合写作者:闫铮,曹雄平,Xiong, Guangyu
通讯作者:Li, Fang