Hi,

I am doing EEG analysis and computing ERP grand average to compare between two of my conditions (say A and B). I want to do a permutation statistical test to see any difference between the two conditions. I think the right function to use is mne.stats.permutation_t_test, however, because I do in parallel the test for each sample in my ERP, the documentation says the p-values are automatically corrected for multiple comparisons by the â€śtmaxâ€ť method. **Is there a way to disable the p-values correction to manually correct it with FDR correction later ?**

Here is what my code looks like for now :

```
# shape is n_subject x n_samples
# cond_A_mean_erp[i, j] is the mean ERP of
# subject i at sample j for the condition A
cond_A_mean_erp = [...]
cond_B_mean_erp = [...]
# data.shape = n_subject x n_samples
# data[i, j] = cond_A[i,j] - cond_B[i,j]
data = [...]
n_permutations = 2000
_, p_values, _ = mne.stats.permutation_t_test(data, n_permutations)
```

The only solution I have seen for now is to call the function one sample at a time but that seems inefficient.

Thank you for your help and I hope that I am clear.

- MNE-Python version: 0.22.1
- operating system: Windows 10