use more liberal thresholds
@@ -146,8 +146,8 @@ def spike_regressors(data, criteria=None, header_prefix='motion_outlier',
value of the variable corresponding to the key exceeds the threshold
indicated by the value, then a spike regressor is created for that
frame. By default, the strategy from Power 2014 is implemented: any
- frames with FD greater than 0.2 or DV greater than 20 are flagged for
- censoring.
+ frames with FD greater than 0.5 or standardised DV greater than 1.5
+ are flagged for censoring.
header_prefix: str
The prefix used to indicate spike regressors in the output data table.
lags: list(int)
@@ -181,8 +181,8 @@ def spike_regressors(data, criteria=None, header_prefix='motion_outlier',
mask = {}
indices = range(data.shape[0])
lags = lags or [0]
- criteria = criteria or {'framewise_displacement': ('>', 0.2),
- 'dvars': ('>', 20)}
+ criteria = criteria or {'framewise_displacement': ('>', 0.5),
+ 'std_dvars': ('>', 1.5)}
for metric, (criterion, threshold) in criteria.items():
if criterion == '<':
mask[metric] = set(np.where(data[metric] < threshold)[0])
