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Detecting semantic priming at the single-trial level.

ABSTRACT: Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N ?=?12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.

SUBMITTER: Geuze J

PROVIDER: S-EPMC3615017 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Detecting semantic priming at the single-trial level.

Geuze Jeroen J van Gerven Marcel A J MA Farquhar Jason J Desain Peter P

PloS one 20130402 4

Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier ...[more]

PMID: 23565237

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