论文标题
使用选定的大脑连接表示的静止状态脑电图分类
Resting-state EEG sex classification using selected brain connectivity representation
论文作者
论文摘要
对潜在临床应用的EEG信号有效分析仍然是一项艰巨的任务。到目前为止，脑电图的分析和条件在很大程度上是性别中性的。本文采用一种机器学习方法来探索性别对脑电图信号的影响的证据，并通过实现静止状态脑电图信号的成功性别预测来证实这些影响的普遍性。我们发现，某些传感器通道之间的连贯性代表的大脑连通性是性别的良好预测指标。
Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. This paper employs a machine learning approach to explore the evidence of sex effects on EEG signals, and confirms the generality of these effects by achieving successful sex prediction of resting-state EEG signals. We have found that the brain connectivity represented by the coherence between certain sensor channels are good predictors of sex.
