2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.LG2026
A Proof-of-Concept Study of Weakly Supervised Labeling of Fine-Grained EEG Components for Artifact Attenuation
Lu Wang-Nöth, Hai Huang, Philipp Heiler +3
Electroencephalography (EEG) is highly susceptible to electromyographic (EMG) artifacts, whose temporal heterogeneity and spatial-spectral overlap with neural activity can leave mi…
cs.CV2026
Not All Confusion Is Equal: A Source-Aware Uncertainty Diagnosis for Fine-Grained Aircraft Detection
Hai Huang, Helmut Mayer
Fine-grained object detectors are commonly evaluated with confusion matrices, which show where the model is confused but not why, nor whether the confusion can be reduced. We argue…
eess.SP2024★ 2 cited
How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings
Lu Wang-Nöth, Philipp Heiler, Hai Huang +5
Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, various artifacts, particularly bio…