EEG variability: Task-driven or subject-driven signal of interest?

• Published in: NeuroImage (Orlando, Fla.) Vol. 252; p. 119034
• Main Authors: Gibson, Erin, Lobaugh, Nancy J., Joordens, Steve, McIntosh, Anthony R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2022; Elsevier Limited; Elsevier
• Subjects: Adult; Behavior; Brain - physiology; Brain signal variability; Cognition; Cognition & reasoning; Cognitive ability; EEG; Electroencephalography; Humans; Individuality; Memory; Motor skill learning; Rest; Skill learning; Variation; Cognition; Brain signal variability; Behavior; EEG; Skill learning
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.119034

•Skill learning had a relatively modest effect on EEG variability across blocks despite having a robust effect on behavior, cognitive strategy and EEG signal strength.•Individual differences in EEG variability, but not EEG signal strength, related strongly to behavior.•The relationship between EEG variability and behavior was mediated by stable indicators of subject identity rather than dynamic indicators of subject performance.•EEG variability provided a highly sensitive subject-driven measure of individual differences. Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest.