R Package: eegwhiten
Published:
R Package Developer
A comprehensive toolkit implementing various whitening transformations (PCA, ZCA, Cholesky) for EEG signal preprocessing.
Posts by Collection
portfolio
R Package: DA4BCI
Published:
R Package Developer
Implemented geometric data augmentation techniques on the Riemannian manifold to improve Motor Imagery EEG classification.
Kaggle Silver Medal: HMS - Harmful Brain Activity Classification
Published:
Rank: 98/2767 (Top 4%) | Silver Medal
Developed a deep learning pipeline using EfficientNet and Weighted Ensembling to classify seizures and harmful brain patterns from EEG signals.
R Package: TensorEEG
Published:
R Package Developer
A physics-constrained simulation engine for generating synthetic 3rd-order EEG tensors ($Time imes Space imes Trial$) with ground-truth validation.
research
Confidence-Gated Adaptation for Cross-Session BCI: A Decision-Oriented Framework
Working Paper (Under Review)
Introducing a ‘Confidence-Interval Gating’ mechanism to quantify uncertainty in distributional shift measurements, enabling robust strategy selection for BCI decoding.
Drift-Feature-Performance Decomposition in MI-EEG via Structured Geometric Modeling
Working Paper (Under Review)
A geometric framework that diagnoses BCI performance degradation by separating signal drift into raw sensor variability and feature-space distortions.
Balancing Performance and Stability in Motor Imagery EEG Decoding: A Linear-First Approach
Working Paper (Under Review)
Proposing a ‘Linear-First’ decision rule using Paired Non-Inferiority Tests (TOST) to balance decoding accuracy against computational cost.
Tensor Multiple Canonical Correlation Analysis (TMCCA): A Framework for Multi-View High-Dimensional Data Integration
Working Paper (In Preparation)
A novel tensor-based statistical framework extending MCCA to high-dimensional datasets, preserving structural information in multi-view neuroimaging analysis.
talks
Benchmarking Classification Pipelines Within and Across Sessions on the PhysioNet EEG Dataset
Published:
Authors: Yiming Shen, David Degras (University of Massachusetts Boston)