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

Figures

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  Fig. 1. Example of raw, clean, and semi-synthetic EEG signals at the frontal electrode Fpz. The semi-synthetic signal preserves the temporal structure of the raw EEG while reproducing prominent ocular artifact characteristics typical of frontal regions. Power spectral density (PSD) analysis shows similar spectral profiles between raw and semi-synthetic signals, with higher power at lower frequencies, while the clean signal exhibits reduced amplitude and low-frequency power due to artifact removal.

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  Fig. 2. Example of raw, clean, and semi-synthetic EEG signals at the central electrode Cz. At Cz, where artifact contamination is relatively low, the raw, semi-synthetic, and clean signals exhibit similar temporal and spectral characteristics.

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  Fig. 3. Example of raw, clean, and semi-synthetic EEG signals at the temporal electrode T8. PSD analysis shows that the semi-synthetic signal preserves spectral characteristics similar to the raw EEG, particularly in higher frequency ranges associated with muscle activity, while the clean signal exhibits reduced power and a smoother spectral profile following artifact removal.

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  Fig. 4. McNemar’s test results for per-label classification performance across the three training datasets on the motor execution session test data. The top row (a) compares Raw EEG with SSDLabeler, the middle row (b) compares Raw EEG with the Previous Work’s SSD, and the bottom row (c) compares the Previous Work’s SSD with SSDLabeler. In all panels, columns correspond to artifact categories in the following order: Clean, Eye, Muscle, Channel, and Other.

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  Fig. 5. McNemar’s test results for per-label classification performance across the three training datasets on the noise session test data. The top row (a) compares Raw EEG with SSDLabeler, the middle row (b) compares Raw EEG with the Previous Work’s SSD, and the bottom row (c) compares the Previous Work’s SSD with SSDLabeler. In all panels, columns correspond to artifact categories in the following order: Clean, Eye, Muscle, Channel, and Other.

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  Fig. 6. McNemar’s test results for per-label classification performance across the ablation datasets on the motor execution session test data. The top row (a) compares SSDLabeler with Ablation 1, the middle row (b) compares SSDLabeler with Ablation 2, and the bottom row (c) compares Ablation 1 with Ablation 2. In all panels, columns correspond to artifact categories in the following order: Clean, Eye, Muscle, Channel, and Other.

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  Fig. 7. McNemar’s test results for per-label classification performance across the ablation datasets on the noise session test data. The top row (a) compares SSDLabeler with Ablation 1, the middle row (b) compares SSDLabeler with Ablation 2, and the bottom row (c) compares Ablation 1 with Ablation 2. In all panels, columns correspond to artifact categories in the following order: Clean, Eye, Muscle, Channel, and Other.

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  Fig. 8. Overview of datasets and processing pipeline used in this study. The BCI2000 motor imagery dataset (top, blue) was used exclusively for external hyperparameter optimization of the proposed SSDLabeler framework, using both ME and MI recordings from three subjects31. This design avoids dataset-specific tuning and better validates generalization. The Cho et al. dataset (bottom, gray) was used for SSD generation and artifact classification28. ME and instructed-noise sessions from 42 subjects were used to generate SSD-based training data, while 10 held-out subjects were reserved exclusively for validation and testing. Only the test set was manually annotated to provide ground truth for final evaluation. All datasets underwent identical preprocessing prior to SSD generation and classification, ensuring a fair and externally valid evaluation.

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  Algorithm 1. ICLabel component labeling (A)

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  Fig. 9. Preprocessing and annotation pipeline for semi-synthetic EEG generation using SSDLabeler. Raw EEG was first preprocessed with bandpass (1–50 Hz) and notch filtering (60 Hz), then epoched into three-second segments. ICA and ICLabel were then applied to decompose the signals and assign artifact probabilities to each IC. Artifact ICs with ICLabel scores \ge 0.6 (e.g., eye, muscle, heart, line, channel, or other) were reconstructed and segmented into time windows (A). RMS and PSD thresholding were subsequently applied to ensure the presence of true contamination within each segment (B). The region highlighted in red corresponds to the conventional SSD procedure used in prior work, including preprocessing, ICA decomposition, ICLabel-based classification, and IC reconstruction. The region highlighted in blue represents the novel components introduced in the proposed SSDLabeler framework, specifically the RMS- and PSD-based verification and the generation of multi-label SSD annotations.

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  Algorithm 2. SSDLabeler: multi-artifact semi-synthetic EEG generation (A+B+C).

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  Fig. 10. RMS thresholding procedure used for artifact verification. For all artifact types except muscle, IC-derived signals were segmented into epochs and the RMS was computed for each segment. Epochs with RMS values exceeding the predefined threshold were labeled as artifact-present (1), whereas those below the threshold were labeled as clean (0). The example plot illustrates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RMS_{\text {eye}}$$\end{document}, where segments above the red dashed line are identified as eye-related artifacts.