Guanghui Zhang

The paper investigates semantic matching across word and picture formats using EEG decoding and temporal generalization analyses, finding distinct neural dynamics for each format.

This study evaluates how SVM regularization and cross-validation parameters affect EEG decoding performance in binary and multiclass tasks, finding optimal settings for accuracy and effect size.

This study evaluates how downsampling affects ERP data quality, univariate metrics, and multivariate decoding, finding that lower sampling rates impact univariate measures more than decoding performance.

The paper evaluates how electrode density affects EEG decoding and RSA across three cognitive tasks, finding task-dependent impacts and suggesting low-density setups can be sufficient.

The paper examines how harmonious and disharmonious color combinations affect neural activity and aesthetic judgments using EEG data.

This study evaluates how artifact correction and rejection affect SVM-based EEG decoding performance, finding that their combination rarely improves results but correction may still be necessary to avoid confounds.

The paper develops a method to select optimal filter settings for ERP data by maximizing signal-to-noise ratio while minimizing waveform distortion.

The paper recommends optimal filter cutoffs for seven common ERP components to balance signal-to-noise ratio and waveform distortion across different noise levels.

This paper evaluates artifact correction and rejection methods in ERP research, finding that correction reduces confounds and rejection reduces noise, improving data quality.

The paper assesses ERP data quality across paradigms, participants, and scoring procedures using the SME metric, finding large variations in quality and highlighting the impact of quantification algorithms.

The paper applies single-trial-based temporal principal component analysis to extract event-related potentials from individual subject data, showing improved consistency with fewer trials.

This paper introduces a data-driven method to objectively extract evoked event-related oscillations from ERP data, showing improved detection compared to conventional methods.