1. The ERF data was obtained using ft_timelockanalysis. For the purpose of inspecting your data visually, we also use ft_timelockgrandaverage to calculate the grand average across participants, which can be used for subsequent visualization.

2.FieldTrip does have similar statistical options for frequency data: at the sensor-level we have the ft_freqstatistics function, and on the source-level (statistics on source reconstructed activity), we have the ft_sourcestatistics function, the latter works on data obtained from ft_sourceanalysis).

3.In experiments, the data is usually collected in different experimental conditions, and the experimenter wants to know, by means of statistical testing, whether there is a difference in the data observed in these conditions. In statistics, a result (for example, a difference among conditions) is statistically significant if it is unlikely to have occurred by chance according to a predetermined threshold probability, the significance level.

4. If the experimenter is interested in a difference in the signal at a certain time-point and sensor, then the more widely used parametric tests are also sufficient.If it is not possible to predict where the differences are, then many statistical comparisons are necessary which lead to the multiple comparisons problem (MCP).A solution of the MCP requires a procedure that controls the FWER at some critical alpha-level (typically, 0.05 or 0.01).

5. When parametric statistics are used, one method that addresses this problem is the so-called Bonferroni correction. The idea is if the experimenter is conducting n number of statistical tests then each of the individual tests should be tested under a significance level that is divided by n. The Bonferroni correction was derived from the observation that if n tests are performed with an alpha significance level then the probability that one comes out significantly is =< n*alpha (Boole’s inequality). In order to keep this probability lower, we can use an alpha that is divided by n for each test. However, the correction comes at the cost of increasing the probability of false negatives, i.e. the test does not have enough power to reveal differences among conditions.

6.  planar gradient is specific for MEG datasets that have axial/magnetometer sensors (CTF, Elekta)

7.he format for these variables, are a prime example of how you should organise your data to be suitable for ft_XXXstatistics. Specifically, each variable is a cell array of structures, with each subject’s averaged stored in one cell. To create this data structure two steps are required. First, the single-subject averages were calculated individually for each subject using the function ft_timelockanalysis. Second, using a for-loop we have combined the data from each subject, within each condition, into one variable (allsubj_FIC/allsubj_FC). We suggest that you adopt this procedure as well.