Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals.

ABSTRACT: Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminating these artifacts during preprocessing enhances the performance of multivariate pattern analysis (MVPA; decoding), especially given that artifact rejection reduces the number of trials available for training the decoder. This study aimed to evaluate the impact of artifact-minimization approaches on the decoding performance of support vector machines. Independent component analysis (ICA) was used to correct ocular artifacts, and artifact rejection was used to discard trials with large voltage deflections from other sources (e.g., muscle a

SUBMITTER: Zhang G 

PROVIDER: S-EPMC11888300 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature