mne.preprocessing.annotate_amplitude#

mne.preprocessing.annotate_amplitude(raw, peak=None, flat=None, bad_percent=5, min_duration=0.005, picks=None, *, verbose=None)[source]#

Annotate raw data based on peak-to-peak amplitude.

Creates annotations BAD_peak or BAD_flat for spans of data where consecutive samples exceed the threshold in peak or fall below the threshold in flat for more than min_duration. Channels where more than bad_percent of the total recording length should be annotated with either BAD_peak or BAD_flat are returned in bads instead. Note that the annotations and the bads are not automatically added to the Raw object; use set_annotations() and info['bads'] to do so.

Parameters:

rawinstance of Raw: The raw data.
peakfloat | dict | None: Annotate segments based on maximum peak-to-peak signal amplitude (PTP). Valid keys can be any channel type present in the object. The values are floats that set the maximum acceptable PTP. If the PTP is larger than this threshold, the segment will be annotated. If float, the minimum acceptable PTP is applied to all channels.
flatfloat | dict | None: Annotate segments based on minimum peak-to-peak signal amplitude (PTP). Valid keys can be any channel type present in the object. The values are floats that set the minimum acceptable PTP. If the PTP is smaller than this threshold, the segment will be annotated. If float, the minimum acceptable PTP is applied to all channels.
bad_percentfloat: The percentage of the time a channel can be above or below thresholds. Below this percentage, Annotations are created. Above this percentage, the channel involved is return in bads. Note the returned bads are not automatically added to info['bads']. Defaults to 5, i.e. 5%.
min_durationfloat: The minimum duration (sec) required by consecutives samples to be above peak or below flat thresholds to be considered. to consider as above or below threshold. For some systems, adjacent time samples with exactly the same value are not totally uncommon. Defaults to 0.005 (5 ms).
picksstr | array_like | slice | None: Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g., ['meg', 'eeg']) will pick channels of those types, channel name strings (e.g., ['MEG0111', 'MEG2623'] will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick good data channels. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.
verbosebool | str | int | None: Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:

annotationsinstance of Annotations: The annotated bad segments.
badslist: The channels detected as bad.

Notes

This function does not use a window to detect small peak-to-peak or large peak-to-peak amplitude changes as the reject and flat argument from Epochs does. Instead, it looks at the difference between consecutive samples.

When used to detect segments below flat, at least min_duration seconds of consecutive samples must respect abs(a[i+1] - a[i]) ≤ flat.
When used to detect segments above peak, at least min_duration seconds of consecutive samples must respect abs(a[i+1] - a[i]) ≥ peak.

Thus, this function does not detect every temporal event with large peak-to-peak amplitude, but only the ones where the peak-to-peak amplitude is supra-threshold between consecutive samples. For instance, segments experiencing a DC shift will not be picked up. Only the edges from the DC shift will be annotated (and those only if the edge transitions are longer than min_duration).

This function may perform faster if data is loaded in memory, as it loads data one channel type at a time (across all time points), which is typically not an efficient way to read raw data from disk.

New in version 1.0.