Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming.

ABSTRACT: Data-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces,

SUBMITTER: Li Y 

PROVIDER: S-EPMC11655819 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature