# events shift after raw.add\_channels **URL:** \ **Category:** Support & Discussions\ **Tags:** annotations\ **Created:** [August 26, 2021, 6:16am UTC](https://mne.discourse.group/t/events-shift-after-raw-add-channels/3591 "2021-08-26T06:16:15Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![GanshengT](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/ganshengt/32/646_2.png) [@GanshengT](https://mne.discourse.group/u/GanshengT)\ **Post date:** [August 26, 2021, 6:16am UTC](https://mne.discourse.group/t/events-shift-after-raw-add-channels/3591/1 "2021-08-26T06:16:15Z")
**If you have a question or issue with MNE-Python, please include the following info:** - MNE-Python version: 0.23.0 - operating system: windows Hi! I found out that the events shift after add\_channels operations. The processing includes cropping the EEG signal according to the experimental paradigm with the following codes. The onset point is stored in the **alighmentInfo**. I plot the **eeg\_raw** before and after cropping to see whether after cropping, the events still match the signals. And the answer is yes. It seems that events from annotations do not change, what changes is the **eeg\_raw. first\_time**. ```python # read and cut EEG # eeg_raw = mne.io.read_raw_eeglab(eeg_fName, preload=True) eeg_raw.plot() eeg_raw.set_montage('standard_1020') eeg_raw.crop(tmin=alignmentInfo.loc[(alignmentInfo['sessionIdx'] == session_idx) & (alignmentInfo['contraction_type'] == 'iVC'), 'EEG'].values[0] / eeg_raw.info['sfreq']) eeg_raw.plot() events, events_id = mne.events_from_annotations(eeg_raw) ``` Then I would add EMG channels into the dataset. Basically, this step includes reading, cropping, and filtering the EMG signals. ```python # read and cut EMG ch_types = ['emg'] * 8 emg_ch_names = ['FDS', 'FCU', 'FCR', 'ECU', 'ECRL', 'BBS', 'TBL', 'LD'] info = mne.create_info(ch_names=emg_ch_names, sfreq=sfreq_emg, ch_types=ch_types) emg_data = pd.read_csv(emg_fName, header=None, skiprows=3, sep=' ', usecols=np.arange(0, 8), skipfooter=0, engine='python') emg_data = emg_data.iloc[alignmentInfo.loc[(alignmentInfo['sessionIdx'] == session_idx) & (alignmentInfo['contraction_type'] == 'iVC'), 'EMG'].values[0]:, :].values emg_data = emg_data.T / 1e6 # uV to V emg_raw = mne.io.RawArray(emg_data, info) emg_raw.filter(l_freq=10, h_freq=250, picks='all') emg_raw.resample(sfreq=sfreq_eeg) # data alighment t_end = min(len(eeg_raw), len(emg_raw)) / sfreq_eeg - 1/sfreq_eeg # find the shortest length of EEG and EMG emg_raw.crop(tmax=t_end) eeg_raw.crop(tmax=t_end) emg_raw.info['highpass'] = eeg_raw.info['highpass'] # In reality, EEGs are bandpassed from 1-45Hz, EMGs are bandpassed from 10-200Hz emg_raw.info['lowpass'] = eeg_raw.info['lowpass'] eeg2merge_raw = eeg_raw.copy() ``` Here’s the problematic function. `aligned_raw = eeg2merge_raw.add_channels([emg_raw])` After doing _add\_channels_, the events no longer match the signals. I look deeper into what changed. It seems that the _aligned\_raw.first\_time_ is as same as _eeg\_raw.first\_time_. However, **mne.events\_from\_annotations(aligned\_raw)** = **mne.events\_from\_annotations(aligned\_raw)** + _aligned\_raw.first\_time_. I think this could be misleading for the users of MNE as the changes is not presumably intended. At the end, I resolve the issue with the following codes. ```python # calibrate annotation onsets = events[:, 0] / sfreq_eeg - aligned_raw.first_time durations = events[:, 1] descriptions = events[:, 2] annotations = mne.Annotations(onsets, durations, descriptions) aligned_raw.set_annotations(annotations) ``` I would like to know what happens when we do the **add\_channels** operation. ---
**Author:** ![richard](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/richard/32/15_2.png) [@richard](https://mne.discourse.group/u/richard)\ **Post date:** [August 26, 2021, 8:13am UTC](https://mne.discourse.group/t/events-shift-after-raw-add-channels/3591/2 "2021-08-26T08:13:45Z")
Hello @GanshengT, do you think you could produce a minimal working example (MWE) based on the MNE `sample` dataset (or any other publicly available data – could also be your own dataset if you are okay with sharing it) to demonstrate the issue? I had a bit of trouble following what exactly you’re doing and which lines are important and which aren’t. An MWE would help others better understand what and where things are going wrong exactly. Thank you, Richard ---
**Author:** ![GanshengT](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/ganshengt/32/646_2.png) [@GanshengT](https://mne.discourse.group/u/GanshengT)\ **Post date:** [August 27, 2021, 5:48am UTC](https://mne.discourse.group/t/events-shift-after-raw-add-channels/3591/3 "2021-08-27T05:48:07Z")
Of course, here is an MWE. I uploaded files used to reproduce the same results. Please download them at [CM-graph/sample\_data/subj61 at master · GanshengT/CM-graph · GitHub](https://github.com/GanshengT/CM-graph/tree/master/sample_data/subj61). The first thing I do is reading the EEG data. ```python import pandas as pd import numpy as np import mne eeg_fName = 'subj61/EEG/subj61_iVC_s01.set' # please define fName accordingly emg_fName = 'subj61/EMG/subj61_iVC_s01.txt' alignmentInfo_fName = 'subj61/subj61_alignmentInfo.txt' eeg_raw = mne.io.read_raw_eeglab(eeg_fName, preload=True) eeg_raw.set_montage('standard_1020') eeg_raw.filter(l_freq=1, h_freq=45) eeg_raw.plot(n_channels=5, scalings=1e-4, duration=15, start=25) ``` Here is what the raw EEG data looks like. ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/66b628e4b782637e3eced440249be5ab0f294431.jpeg) The next thing I do is cropping the raw EEG data based on the alignment information. In fact, the EEG begins few seconds prior to the EMG recording. ```python alignmentInfo = pd.read_csv(alignmentInfo_fName, skiprows=0, sep=',', engine='python') eeg_raw.crop(tmin=alignmentInfo.loc[(alignmentInfo['sessionIdx'] == 's01') & (alignmentInfo['contraction_type'] == 'iVC'), 'EEG'].values[0] / eeg_raw.info['sfreq']) eeg_raw.plot(n_channels=5, scalings=1e-4, duration=15, start=25) ``` And here is the result. So far so good, the events and the signals were shifted forward simultaneously. ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/d3881cc08382d5f07ac769c29184737689cf0106.jpeg) When I concatenate the EEG data and the EMG data, the problem occurs. ```python ch_types = ['emg'] * 8 emg_ch_names = ['FDS', 'FCU', 'FCR', 'ECU', 'ECRL', 'BBS', 'TBL', 'LD'] info = mne.create_info(ch_names=emg_ch_names, sfreq=1000, ch_types=ch_types) emg_data = pd.read_csv(emg_fName, header=None, skiprows=3, sep=' ', usecols=np.arange(0, 8), skipfooter=0, engine='python') emg_data = emg_data.iloc[alignmentInfo.loc[(alignmentInfo['sessionIdx'] == 's01') & (alignmentInfo['contraction_type'] == 'iVC'), 'EMG'].values[0]:, :].values emg_data = emg_data.T / 1e6 # uV to V emg_raw = mne.io.RawArray(emg_data, info) emg_raw.filter(l_freq=10, h_freq=250, picks='all') emg_raw.resample(sfreq=500) t_end = min(len(eeg_raw), len(emg_raw)) / 500 - 1 / 500 # find the shortest length of EEG and EMG emg_raw.crop(tmax=t_end) eeg_raw.crop(tmax=t_end) emg_raw.info['highpass'] = eeg_raw.info['highpass'] # In reality, EEGs are bandpassed from 1-45Hz, EMGs are bandpassed from 10-200Hz emg_raw.info['lowpass'] = eeg_raw.info['lowpass'] eeg2merge_raw = eeg_raw.copy() aligned_raw = eeg2merge_raw.add_channels([emg_raw]) aligned_raw.plot(n_channels=5, scalings=1e-4, duration=15, start=25) ``` As shown in the figure below, we see the data (signals) were shifted forwarded as expected. But the events did not. ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/6bf55bca79cccfce844cebcaa69928a434342579.jpeg) ---
**Author:** ![GanshengT](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/ganshengt/32/646_2.png) [@GanshengT](https://mne.discourse.group/u/GanshengT)\ **Post date:** [August 27, 2021, 5:51am UTC](https://mne.discourse.group/t/events-shift-after-raw-add-channels/3591/4 "2021-08-27T05:51:15Z")
BTW, I also tested with mne dataset. But this time everything works fine. I wonder whether there is a problem with data imported from EEGLAB? Here is an MWE. ```python from mne.io import concatenate_raws, read_raw_edf from mne.datasets import eegbci import mne %matplotlib qt sfreq=160 raw_fnames = eegbci.load_data(1, 1) raws = [read_raw_edf(f, preload=True) for f in raw_fnames] raw = concatenate_raws(raws) # define 2 events onsets = [4, 8] durations = [0, 0] descriptions = ['event1', 'event2'] annotations = mne.Annotations(onsets, durations, descriptions) raw.set_annotations(annotations) raw.plot(n_channels=5, scalings=1e-4, duration=10) ``` ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/5169dc5b895ac294b63988834a62bdd3c5c19e53.jpeg) ```python emg_data = raw.get_data()[1:3, :] # define 2 emg chs ch_types = ['emg'] * 2 emg_ch_names = ['emg1', 'emg2'] info = mne.create_info(ch_names=emg_ch_names, sfreq=sfreq, ch_types=ch_types) emg_raw = mne.io.RawArray(emg_data, info) raw.crop(tmin=3) raw.plot(n_channels=5, scalings=1e-4, duration=10) ``` ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/9fa5a2892dd2953de15fa8137822ba81de5292f1.jpeg) ```python # align eeg and emg data t_end = min(len(raw),emg_data.shape[1]) / sfreq - 1 / sfreq emg_raw.crop(tmax=t_end) raw.crop(tmax=t_end) eeg_raw = raw.copy() aligned_raw = eeg_raw.add_channels([emg_raw]) aligned_raw.plot(n_channels=5, scalings=1e-4, duration=10) ``` ![image](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/2170b985512cc4b62ab38e846631eb4c74728aa7.jpeg)