R Package: eegwhiten
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R Package Developer
Whitening and covariance-alignment tools for EEG features, with reusable fit/transform models, shrinkage, robust covariance estimators, Euclidean alignment, and tangent-space utilities.
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R Package Developer
Whitening and covariance-alignment tools for EEG features, with reusable fit/transform models, shrinkage, robust covariance estimators, Euclidean alignment, and tangent-space utilities.
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R Package Developer
A unified R framework for EEG/BCI domain adaptation, including TCA, SA, CORAL, GFK, MIDA, Riemannian transport, optimal transport, and shift metrics.
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R Package Developer
A train/test-safe EEG feature extraction toolkit for BCI pipelines, including CSP/FBCSP, tangent-space covariance features, bandpower, Hjorth, ATM, MVAR, and MSVAR features.
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R Package Developer
A physics-constrained EEG simulator and covariance-aware augmentation audit package for cross-session BCI research.
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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.
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Python Package Developer
Python implementation of DA4BCI, providing EEG/BCI domain adaptation methods, shift metrics, SPD geometry tools, Page-Hinkley drift detection, and plotting utilities.
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Python Package Developer
Python port of TensorEEG for physics-constrained EEG simulation, SPD covariance augmentation, fidelity auditing, and manifest replay.
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Python Package Developer
Reproducible comparison of pooling, weighting, and source-selection strategies for transfer across EEG sessions.
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Dashboard Developer
A Streamlit dashboard for comparing multi-source domain adaptation strategies in cross-session EEG classification.
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Dashboard Developer
An offline analysis dashboard separating fixed-model degradation, domain-adaptation benefit, and the remaining gain from retraining on later EEG sessions.
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Application Developer
A sample-paced pseudo-online replay application for motor-imagery EEG, with CSP, FBCSP, tangent-space pipelines, Euclidean Alignment, and progressive performance visualization.
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Application Developer
A dependency-free browser visualizer for understanding how domain adaptation methods move source and target distributions.
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Interactive electrode-layout design with bounded optimization, forward simulation, and model-based evaluation.
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An experimental macOS application integrating local person tracking, camera capture, and Bluetooth gimbal control.
Manuscript in Preparation
Manuscript in preparation
A tensor-based statistical framework extending MCCA to high-dimensional datasets, preserving structural information in multi-view neuroimaging analysis.
Under Review
Manuscript under review
Proposing a ‘Linear-First’ decision rule using Paired Non-Inferiority Tests (TOST) to balance decoding accuracy against computational cost.
Under Review
Manuscript under review
A geometric framework that diagnoses BCI performance degradation by separating signal drift into raw sensor variability and feature-space distortions.
Under Review
Manuscript under review (solo author)
A cross-participant study of MI-EEG system selection: single decoders, uncertainty-routed decoder pairs, and ensembles compared by final accuracy and execution cost, with calibration-based selection tested on held-out participants.
Published · Frontiers in Human Neuroscience - Brain-Computer Interfaces
Published in Frontiers in Human Neuroscience, 28 Aug 2026
A matched comparison of global and selective source-session strategies for within-subject, cross-session motor-imagery EEG transfer.
Recommended citation: Shen Y and Degras D (2026) A confidence-gated source selection strategy for cross-session transfer in brain–computer interfaces. Front. Hum. Neurosci. 20:1895016. doi:10.3389/fnhum.2026.1895016. https://doi.org/10.3389/fnhum.2026.1895016
Ph.D. Dissertation · University of Massachusetts Boston
Ph.D. Dissertation, University of Massachusetts Boston (Aug 2026)
Reliable MI-EEG decoding as a sequence of representation, transfer, drift measurement, adaptation, and recalibration choices.
Published:
Authors: Yiming Shen, David Degras (University of Massachusetts Boston)
Published:
Authors: Yiming Shen, David Degras (University of Massachusetts Boston)
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Presenter: Yiming Shen
Poster number: G5-Tues