mne.stats.erp.compute_peak

mne.stats.erp.compute_peak(evoked, start=None, stop=None, picks='all', mode='abs', average=False, strict=True)

Compute the peak amplitude and latency of an evoked response.

Parameters:

evokedinstance of Evoked

The evoked response object.

start, stopfloat

Start and end time of the ERP computation window in seconds. Defaults to None and None, which corresponds to the entire Evoked object.

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 all channels. Bad channels are included by default. Note that channels in info['bads'] will be included if their names or indices are explicitly provided.

modestr

Specifies how the peak amplitude should be determined. Can be one of:

'abs'

The peak amplitude is the maximum absolute value.

'neg'

The peak amplitude is the maximum negative value. If there are no negative values and strict is True, a ValueError is raised.

'pos'

The peak amplitude is the maximum positive value. If there are no positive values and strict is True, a ValueError is raised.

Defaults to 'abs'.

averagebool

If True, the peak amplitude is computed by averaging the data across channels before finding the peak. Defaults to False.

strictbool

If True, raise an error if values are all positive when detecting a minimum (mode=’neg’), or all negative when detecting a maximum (mode=’pos’). Defaults to True.

Returns:

peak_dfpandas.DataFrame

A DataFrame with columns ‘channel’, ‘latency’, and ‘amplitude’ containing the peak amplitude and latency for each channel. If average=True, contains a single row whose ‘channel’ value is 'Average'.