ArticleFrontiers in neuroscience2021
Quantifying Signal Quality From Unimodal and Multimodal Sources: Application to EEG With Ocular and Motion Artifacts.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- CLSP-REQA: a real-time quality-aware closed-loop seizure prediction framework with Mamba-BiLSTM and confidence-gated intervention.Frontiers in neuroinformatics · 2026Article
- Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.Frontiers in human neuroscience · 2023Review
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Authors and funding
2 authors.
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Abstract
With prevalence of electrophysiological data collected outside of the laboratory from portable, non-invasive modalities growing at a rapid rate, the quality of these recorded data, if not adequate, could affect the effectiveness of medical devices that depend of them. In this work, we propose novel methods to evaluate electrophysiological signal quality to determine how much of the data represents the physiological source of interest. Data driven models are investigated through Bayesian decision and deep learning-based methods to score unimodal (signal and noise recorded on same device) and multimodal (signal and noise each recorded from different devices) data, respectively. We validate these methods and models on three electroencephalography (EEG) data sets (
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