EEG Data Quality in Real-World Settings: Examining Neural Correlates of Attention in School-Aged Children

Abstract

Advances in mobile electroencephalography (EEG) technology have made it possible to examine covert cognitive processes in real-world settings such as student attention in the classroom. Here, we outline research using wired and wireless EEG technology to examine attention in elementary school children across increasingly naturalistic paradigms in schools, ranging from a lab-based paradigm where children met one-on-one with an experimenter in a field laboratory to mobile EEG testing conducted in the same school during semi-naturalistic classroom lessons. Despite an increase of data loss with the classroom-based paradigm, we demonstrate that it is feasible to collect quality data in classroom settings with young children. We also provide a test case for how robust EEG signals, such as alpha oscillations, can be used to identify measurable differences in covert processes like attention in classrooms. We end with pragmatic suggestions for researchers interested in employing naturalistic EEG methods in real-world, multisensory contexts.

Fig. 1.

Effects of Tasks (Study 1) or Instructional Activities (Study 2) on Attention as Indexed by Normalized Alpha Power. (a) Topographic maps for alpha power in study 1 revealed that alpha power was higher when children watched the MSB video and had a posterior scalp distribution. (b) Student attention was measured as mean normalized alpha power for each lab-based task, with error bars representing 95% confidence interval (calculated by bootstrapping). (c) Topographic maps for alpha power in study 2 revealed that alpha power was higher during the mindfulness session and had a posterior scalp distribution. (d) Student attention was measured as mean normalized alpha power for each class activity, with error bars representing 95% confidence interval (calculated by bootstrapping). *p < .05, **p < .01, ***p < .001.