Decoding of task-relevant and task-irrelevant intracranial EEG representations

Natural stimuli consist of multiple properties. However, not all of these properties are equally relevant in a given situation. In this study, we applied multivariate classification algorithms to intracranial electroencephalography data of human epilepsy patients performing an auditory Stroop task. This allowed us to identify neuronal representations of task-relevant and irrelevant pitch and semantic information of spoken words in a subset of patients. When properties were relevant, representations could be detected after about 350ms after stimulus onset. When irrelevant, the association with gamma power differed for these properties. Patients with more reliable representations of irrelevant pitch showed increased gamma band activity (35-64Hz), suggesting that attentional resources allow an increase in gamma power in some but not all patients. This effect was not observed for irrelevant semantics, possibly because the more automatic processing of this property allowed for less variation in free resources. Processing of different properties of the same stimulus seems therefore to be dependent on the characteristics of the property.

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