algorithms.event_related¶
Module: algorithms.event_related
¶
Eventrelated analysis
Functions¶

nitime.algorithms.event_related.
fir
(timeseries, design)¶ Calculate the FIR (finite impulse response) HRF, according to [Burock2000]
Parameters: timeseries : float array
timeseries data
design : int array
This is a design matrix. It has to have shape = (number of TRS, number of conditions * length of HRF)
The form of the matrix is:
A B C …
where A is a (number of TRs) x (length of HRF) matrix with a unity matrix placed with its top left corner placed in each TR in which event of type A occurred in the design. B is the equivalent for events of type B, etc.
Returns: HRF: float array :
HRF is a numpy array of 1X(length of HRF * number of conditions) with the HRFs for the different conditions concatenated. This is an estimate of the linear filters between the timeseries and the events described in design.
Notes
Implements equation 4 in Burock(2000):
\[\hat{h} = (X^T X)^{1} X^T y\]M.A. Burock and A.M.Dale (2000). Estimation and Detection of EventRelated fMRI Signals with Temporally Correlated Noise: A Statistically Efficient and Unbiased Approach. Human Brain Mapping, 11:249260

nitime.algorithms.event_related.
freq_domain_xcorr
(tseries, events, t_before, t_after, Fs=1)¶ Calculates the event related timeseries, using a crosscorrelation in the frequency domain.
Parameters: tseries: float array :
Time series data with time as the last dimension
events: float array :
An array with timeresolved events, at the same sampling rate as tseries
t_before: float :
Time before the event to include
t_after: float :
Time after the event to include
Fs: float :
Sampling rate of the timeseries (in Hz)
Returns: xcorr: float array :
The correlation function between the tseries and the events. Can be interperted as a linear filter from events to responses (the timeseries) of an LTI.

nitime.algorithms.event_related.
freq_domain_xcorr_zscored
(tseries, events, t_before, t_after, Fs=1)¶ Calculates the zscored event related timeseries, using a crosscorrelation in the frequency domain.
Parameters: tseries: float array :
Time series data with time as the last dimension
events: float array :
An array with timeresolved events, at the same sampling rate as tseries
t_before: float :
Time before the event to include
t_after: float :
Time after the event to include
Fs: float :
Sampling rate of the timeseries (in Hz)
Returns: xcorr: float array :
The correlation function between the tseries and the events. Can be interperted as a linear filter from events to responses (the timeseries) of an LTI. Because it is normalized to its own mean and variance, it can be interperted as measuring statistical significance relative to all timeshifted versions of the events.