API reference#
DSS#
Compute DSS spatial filters from baseline and biased covariances. |
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Denoising Source Separation (DSS) Transformer. |
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Extract multiple DSS components using iterative (nonlinear) algorithm. |
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Iterative (Nonlinear) Denoising Source Separation Transformer. |
ZapLine#
ZapLine Transformer for line noise removal. |
iCanClean#
ICanClean Transformer for reference-based artifact removal. |
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Compute one iCanClean pass on continuous NumPy arrays. |
Denoisers#
Base class for linear bias functions. |
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Bias function for finding repeatable components via averaging. |
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Bias for removing quasi-periodic artifacts (e.g., ECG, EOG). |
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Bandpass filter bias for narrow-band rhythm extraction. |
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A bias LinearDenoiser for line noise isolation (Notch/IIR or FFT/Harmonic). |
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Peak filter bias for single-frequency extraction. |
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Comb filter bias for harmonic frequency extraction. |
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Time-shift bias for extracting autocorrelated signals. |
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Unified temporal smoothing bias (Moving Average). |
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Linear spectrogram bias (Section 4.1.3). |
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Base class for nonlinear/adaptive denoiser functions. |
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Tanh mask denoiser (Standard FastICA nonlinearity). |
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Robust tanh denoiser (FastICA / RobustICA formulation). |
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Kurtosis maximization denoiser. |
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Skewness nonlinearity (FastICA 'skew'). |
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Gaussian nonlinearity (FastICA 'gauss'). |
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Adaptive Wiener mask denoiser. |
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Adaptive/Nonlinear spectrogram denoiser (Section 4.1.3). |
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DCT domain denoiser (MATLAB denoise_dct.m). |
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Quasi-periodic denoiser via cycle averaging. |
Variants#
Create a DSS configured for temporal predictability. |
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Create a DSS configured for temporally smooth sources. |
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Create a DSS configured for a specific frequency band. |
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Scan frequencies to find optimal narrowband DSS components. |
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Create a DSS configured for SSVEP extraction. |
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Time-Shift DSS (TSR) variant. |
Visualization#
Plot a compact per-component summary dashboard. |
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Plot stacked component time series with fixed vertical offsets. |
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Plot a time-frequency power view for one component. |
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Plot a 1D component score curve for a fitted estimator. |
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Plot per-window score traces from a 2D score matrix. |
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Plot spatial component patterns. |
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Plot component activity as an epoch-by-time image. |
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Plot PSD comparison for original and denoised data. |
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Plot GFP comparison for before/after signals. |
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Plot before/after channel time courses for explicit channel picks. |
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Plot a topomap of preserved power ratio after denoising. |
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Compare before/after spectrograms averaged across selected channels. |
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Overlay one before/after trace to inspect reconstruction quality. |
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Plot input PSD next to PSDs of selected components. |
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Plot group-mean evoked responses with optional SEM bands. |
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Plot score/eigenvalue profiles from a narrowband scan. |
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Visualize a time-frequency mask matrix. |
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Plot grouped bar charts for one or more scalar metrics. |
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Plot a per-window count or metric series. |
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Plot a grouped x/y trade-off scatter with optional group means. |
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Plot one metric as grouped bars or paired subject trajectories. |
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Plot subject-level paired trajectories for one or more metrics. |
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Plot violin + strip distributions with optional paired subject lines. |
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Plot a null-distribution histogram with observed statistic and CI. |
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Plot per-subject point estimates with confidence intervals. |
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Plot grouped per-harmonic attenuation bars for line-noise studies. |
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Plot a two-panel metric trade-off summary. |
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Plot a generic denoising diagnostics dashboard. |
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Plot a generic component-cleaning dashboard. |
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Plot grouped time-domain signal diagnostics. |
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Plot condition-by-group interaction traces. |
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Plot group-wise condition interaction traces. |
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Plot a generic endpoint-metric storyboard. |