Stuttering is a communication disorder that affects approximately 1% of the population. Although 5-8% of preschool children begin to stutter, the majority will recover with or without intervention. There is a significant gap, however, in our understanding of why many children recover from stuttering while others persist and stutter throughout their lives. Detecting neurophysiological biomarkers of stuttering persistence is a critical objective of this paper. In this paper, we developed a novel supervised sparse feature learning approach to discover discriminative biomarkers from functional near infrared spectroscopy (fNIRS) brain imaging data recorded during a speech production experiment from 46 children in three groups: children who stutter ( ); children who do not stutter ( ); and children who recovered from stuttering ( ). We made an extensive feature analysis of the cerebral hemodynamics from fNIRS signals and selected a small number of important discriminative features using the proposed sparse feature learning framework. The selected features are capable of differentiating neural activation patterns between children who do and do not stutter with an accuracy of 87.5% based on a five-fold cross-validation procedure. The discovered set cerebral hemodynamics features are presented as a set of promising biomarkers to elucidate the underlying neurophysiology in children who have recovered or persisted in stuttering and to facilitate future data-driven diagnostics in these children.
Assessment of text relevance is an important aspect of human–information interaction. For many search sessions it is essential to achieving the task goal. This work investigates text relevance decision dynamics in a question‐answering task by direct measurement of eye movement using eye‐tracking and brain activity using electroencephalography EEG. The EEG measurements are correlated with the user's goal‐directed attention allocation revealed by their eye movements. In a within‐subject lab experiment (N = 24), participants read short news stories of varied relevance. Eye movement and EEG features were calculated in three epochs of reading each news story (early, middle, final) and for periods where relevant words were read. Perceived relevance classification models were learned for each epoch. The results show reading epochs where relevant words were processed could be distinguished from other epochs. The classification models show increasing divergence in processing relevant vs. irrelevant documents after the initial epoch. This suggests differences in cognitive processes used to assess texts of varied relevance levels and provides evidence for the potential to detect these differences in information search sessions using eye tracking and EEG.
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