neerajwagh
PhD Student in Bioengineering @ UIUC. MS Statistics @ UIUC. Computer Engineer.
Champaign, Illinois
Pinned Repositories
eeg-gcnn
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
eeg-self-supervision
Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability". Accepted at ML4H Symposium 2021 with an oral spotlight!
evaluating-eeg-representations
Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.
neerajwagh
Config files for my GitHub profile.
tensor_decomp_eeg
Resources for the IEEE NER 2023 paper: https://ieeexplore.ieee.org/abstract/document/10123800. Decomposition of scalp EEG population data into expert-interpretable factors and their use in understanding differences in smaller clinical cohorts.
neuroparc
neerajwagh's Repositories
neerajwagh/eeg-gcnn
Resources for the paper titled "EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network". Accepted for publication (with an oral spotlight!) at ML4H Workshop, NeurIPS 2020.
neerajwagh/eeg-self-supervision
Resources for the paper titled "Domain-guided Self-supervision of EEG Data Improves Downstream Classification Performance and Generalizability". Accepted at ML4H Symposium 2021 with an oral spotlight!
neerajwagh/evaluating-eeg-representations
Resources for the paper titled "Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts". Accepted at NeurIPS 2022.
neerajwagh/neerajwagh
Config files for my GitHub profile.
neerajwagh/tensor_decomp_eeg
Resources for the IEEE NER 2023 paper: https://ieeexplore.ieee.org/abstract/document/10123800. Decomposition of scalp EEG population data into expert-interpretable factors and their use in understanding differences in smaller clinical cohorts.