Li Ziyang, Wang Hong, Song Jianing, Gong Jiale
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.
Senzhigaoke Company Limited, Gaoke Street, Shenyang 110002, China.
Sensors (Basel). 2024 Dec 25;25(1):52. doi: 10.3390/s25010052.
The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT). Time-frequency features were extracted using the Multitaper method, followed by multidimensional reduction techniques. Subspace features (F24 and F216) were selected via -tests and False Discovery Rate (FDR) multiple comparisons correction, and subsequently analyzed in the Time-Frequency Area Average Test (TFAAT) and Prefrontal Beta Time Series Test (PBTST). The experimental results reveal that the MSIT task achieves optimal cross-subject classification performance using the Support Vector Machine (SVM) approach with the TFAAT feature set, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 58%. Similarly, the Sternberg Memory Task demonstrates classification ability with the logistic regression model applied to the PBTST feature set, emphasizing the beta band power spectrum in the prefrontal cortex as a potential marker of AD risk. These findings confirm that task-state EEG provides stronger classification potential compared to resting-state EEG, offering valuable insights for advancing early AD prediction research.
在健康个体中早期预测阿尔茨海默病（AD）风险仍然是一项重大挑战。本研究调查了任务态脑电图（EEG）信号用于提高检测准确性的可行性。从多源干扰任务（MSIT）和斯特恩伯格记忆任务（STMT）中收集脑电图（EEG）数据。使用多 taper 方法提取时频特征，随后采用多维降维技术。通过 t 检验和错误发现率（FDR）多重比较校正选择子空间特征（F24 和 F216），随后在时频区域平均测试（TFAAT）和前额叶β时间序列测试（PBTST）中进行分析。实验结果表明，使用支持向量机（SVM）方法和 TFAAT 特征集，MSIT 任务实现了最佳的跨受试者分类性能，受试者工作特征曲线下面积（ROC AUC）为 58%。同样，斯特恩伯格记忆任务通过将逻辑回归模型应用于 PBTST 特征集展示了分类能力，强调前额叶皮质中的β波段功率谱作为 AD 风险的潜在标志物。这些发现证实，与静息态 EEG 相比，任务态 EEG 具有更强的分类潜力，为推进 AD 早期预测研究提供了有价值的见解。
