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SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG

arXiv — cs.LG•Monday, December 8, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • SSDLabeler has been introduced as a framework for generating realistic semi-synthetic data to enhance multi-label artifact classification in electroencephalography (EEG). This development addresses the limitations of existing artifact classification methods, which often rely on extensive manual labeling and fail to capture the diversity of real-world EEG signals.
  • The introduction of SSDLabeler is significant as it offers a more efficient and realistic approach to artifact classification, potentially improving the preprocessing of EEG data and its applications in various fields, including brain-computer interfaces and neurological research.
  • This advancement reflects a growing trend in the field of EEG research, where innovative methodologies are being developed to enhance data representation and analysis. The integration of semi-synthetic data generation techniques aligns with broader efforts to improve the robustness and generalizability of EEG applications, addressing challenges such as variability in brain signals and the need for extensive labeled datasets.
— via World Pulse Now AI Editorial System
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