L. J. Gabard-Durnam

Cognitive Neuroscience · 2018

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 15 citations into it; #10,203 of 10,852 by trust here, #5,990 by citations
• Signal Processing — 8 citations into it; #7,314 of 7,572 by trust here, #3,566 by citations

Whom L. J. Gabard-Durnam trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Irene Winkler
2. Arnaud Delorme
3. David L. Donoho
4. Scott Makeig
5. Christian Andreas Kothe
6. Jiapeng Xie
7. T. Mullen
8. Nazareth P. Castellanos
9. Tapan Kumar Gandhi
10. A. Mognon

Who trusts L. J. Gabard-Durnam overall

1. C. Wilkinson
2. Haerin Chung
3. Hannah Plueckebaum
4. G. Buzzell
5. Lars Meyer

Papers from L. J. Gabard-Durnam's perspective

Ordered by how strongly the researchers L. J. Gabard-Durnam trusts endorse each paper. Same corpus, a different order for every researcher.

1. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. (Journal of Neuroscience Methods)
2. BCILAB: a platform for brain–computer interface development (Journal of Neural Engineering)
3. ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. (Psychophysiology)
4. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals (Behavioral and Brain Functions)
5. Committee report: publication guidelines and recommendations for studies using electroencephalography and magnetoencephalography. (Psychophysiology)
6. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis (NeuroImage)
7. FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. (Journal of Neuroscience Methods)
8. Robust artifactual independent component classification for BCI practitioners (Journal of Neural Engineering)
9. A well-conditioned estimator for large-dimensional covariance matrices
10. Removing electroencephalographic artifacts by blind source separation. (Psychophysiology)

Their papers

• The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data (2018) — 470 citations