Abstract
Background: 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.Purpose: 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.Methods: 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.Results: 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 (p < 0.001). Gamma RP in the frontal site of FR was weaker than noFR in the background condition (p < 0.001), while alpha RP in parieto-occipital sites of FR was stronger than noFR in the cue condition (p < 0.001).Conclusion: 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.
Highlights
It is common nowadays for us to obtain information from social media, such as from Twitter, Facebook, TikTok, and Weibo, among others
In the final test set, the highest classification accuracy of fragmented reading (FR) was up to 96.52% by SVM model in background condition (F1 = 0.96, Area under the curve (AUC) = 0.95, the highest)
Since gamma-band oscillations have been found involved in the maintenance of working memory (WM) information (Howard et al, 2003; Jensen et al, 2007) and the increase of frontal gamma activity might indicate the enhancement of maintenance of WM (Kaiser and Lutzenberger, 2005b; Roberts et al, 2013), our results suggest that FR might lead to an impairment on WM
Summary
It is common nowadays for us to obtain information from social media, such as from Twitter, Facebook, TikTok, and Weibo, among others. Due to the high spatial resolution of fMRI, it is widely used to study cognition and has made fruitful achievements (Catalino et al, 2020; Williams et al, 2021). Cognitive processes, such as attention, usually evolve fast. Little is known whether fragmented reading (FR) affects cognition and what are the underlying electroencephalogram (EEG) alterations it may lead to. It revealed that alpha and gamma contribute more EFs to the classification of FR and noFR under both conditions. Classification of Fragmented Reading and noFR by Each Frequency Band. The highest classification accuracy of FR reached 86.69% by gamma in the cue condition
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