Feng Jingwen, Hu Bo, Sun Jingting, Zhang Junpeng, Wang Wen, Cui Guangbin
College of Electrical Engineering, Sichuan University, Chengdu, China.
Functional and Molecular Imaging Key Lab of Shaanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Front Hum Neurosci. 2021 Oct 22;15:753735. doi: 10.3389/fnhum.2021.753735. eCollection 2021.
The use of social media daily could nurture a fragmented reading habit. However, little is known whether fragmented reading (FR) affects cognition and what are the underlying electroencephalogram (EEG) alterations it may lead to. This study aimed to identify whether individuals have FR habits based on the single-trial EEG spectral features using machine learning (ML), as well as to find out the potential cognitive impairment induced by FR. Subjects were recruited through a questionnaire and divided into FR and noFR groups according to the time they spent on FR per day. Moreover, 64-channel EEG was acquired in Continuous Performance Task (CPT) and segmented into 0.5-1.5 s post-stimulus epochs under cue and background conditions. The sample sizes were as follows: FR in cue condition, 692 trials; noFR in cue condition, 688 trials; FR in background condition, 561 trials; noFR in background condition, 585 trials. For these single-trials, the relative power (RP) of six frequency bands [delta (1-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta1 (14-20 Hz), beta2 (21-29 Hz), lower gamma (30-40 Hz)] were extracted as features. After feature selection, the most important feature sets were fed into three ML models, namely Support-Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes to perform the identification of FR. RP of six frequency bands was also used as feature sets to conduct classification tasks. The classification accuracy reached up to 96.52% in the SVM model under cue conditions. Specifically, among six frequency bands, the most important features were found in alpha and gamma bands. Gamma achieved the highest classification accuracy (86.69% for cue, 86.45% for background). In both conditions, alpha RP in central sites of FR was stronger than noFR ( < 0.001). Gamma RP in the frontal site of FR was weaker than noFR in the background condition ( < 0.001), while alpha RP in parieto-occipital sites of FR was stronger than noFR in the cue condition ( < 0.001). Fragmented reading can be identified based on single-trial EEG evoked by CPT using ML, and the RP of alpha and gamma may reflect the impairment on attention and working memory by FR. FR might lead to cognitive impairment and is worth further exploration.
每天使用社交媒体可能会养成碎片化阅读习惯。然而，对于碎片化阅读（FR）是否会影响认知以及它可能导致的潜在脑电图（EEG）改变，我们知之甚少。本研究旨在基于单次试验的脑电图频谱特征，使用机器学习（ML）来确定个体是否有FR习惯，并找出FR引起的潜在认知障碍。通过问卷调查招募受试者，并根据他们每天花费在FR上的时间分为FR组和非FR组。此外，在持续操作任务（CPT）中采集64通道脑电图，并在提示和背景条件下将其分割为刺激后0.5 - 1.5秒的时间段。样本量如下：提示条件下的FR组，692次试验；提示条件下的非FR组，688次试验；背景条件下的FR组，561次试验；背景条件下的非FR组，585次试验。对于这些单次试验，提取六个频段[δ（1 - 3Hz）、θ（4 - 7Hz）、α（8 - 13Hz）、β1（14 - 20Hz）、β2（21 - 29Hz）、低γ（30 - 40Hz）]的相对功率（RP）作为特征。经过特征选择后，将最重要的特征集输入到三个ML模型，即支持向量机（SVM）、K近邻（KNN）和朴素贝叶斯，以进行FR的识别。六个频段的RP也用作特征集来进行分类任务。在提示条件下，SVM模型的分类准确率高达96.52%。具体而言，在六个频段中，最重要的特征出现在α和γ频段。γ频段的分类准确率最高（提示条件下为86.69%，背景条件下为86.45%）。在两种条件下，FR组中央部位α频段的RP均强于非FR组（<0.001）。在背景条件下，FR组额叶部位γ频段的RP弱于非FR组（<0.001），而在提示条件下，FR组顶枕部位α频段的RP强于非FR组（<0.001）。使用ML基于CPT诱发的单次试验脑电图可以识别碎片化阅读，α和γ频段的RP可能反映了FR对注意力和工作记忆的损害。FR可能导致认知障碍，值得进一步探索。
