Event's based Classification

[Star (Arjun Rajan)]

Hello,

This is a sample of my dataset.

sampling frequency - 128 Hz

• operating system: Windows 10

Have converted the .csv to .fif but the event column is not getting processed.
Have created a channel “STIM channel” for the events also. But getting error.
ValueError: No stim channels found. Consider specifying them manually using the ‘stim_channel’ parameter.

events = mne.find_events(raw)

How to process the events column of the csv file and process the eeg signals and predict which event based on the 14 channels?

@richard @cbrnr
Thanks

[richard (Richard Höchenberger)]

Hello @Star,

@Star

Have converted the .csv to .fif but the event column is not getting processed.
Have created a channel “STIM channel” for the events also. But getting error.
ValueError: No stim channels found. Consider specifying them manually using the ‘stim_channel’ parameter.

you need to set the channel type of the event channel to stim, otherwise find_events() won’t be able to find it.

So you’ll need to do something like:

raw.set_channel_types({'event': 'stim'})

first.

@Star

and process the eeg signals and predict which event based on the 14 channels?

Please follow our tutorial on MVPA at Decoding (MVPA) — MNE 1.6.0 documentation

Best wishes,
Richard

[Star (Arjun Rajan)]

Hi @richard ,

Thanks,
Now when I run:
events = mne.find_events(raw)

Trigger channel has a non-zero initial value of 1 (consider using initial_event=True to detect this event)
Removing orphaned offset at the beginning of the file.
286 events found
Event IDs: [1 2]

It’s only finding 2 events when there are 4 events totally.
Why is this happening? Not finding all events and also number of events are less.

Thanks

[richard (Richard Höchenberger)]

According to our glossary entry, stim channels only have values >= 0. This probably explains why only events with codes 1 and 2 are being considered.

https://mne.tools/dev/glossary.html#term-stim-channel

[richard (Richard Höchenberger)]

I also have to say I’m surprised the values in that channel fluctuate so much from row to row. What does each row in your input data actually represent?

[Star (Arjun Rajan)]

Hi,

The dataset contains recording of 14 channels and their event/class. There are totally 4 classes: listening, writing, resting etc…
ok.

Event's based Classification

According to our glossary entry, stim channels only have values >= 0. This probably explains why only events with codes 1 and 2 are being considered. https://mne.tools/dev/glossary.html#term-stim-channel

Then it should be 0 ,1 and 2 events right. If the events are 1 and 2 only, then also all the events of 1 and 2 are not being considered.

Yes it fluctuates so much that’s the reason I’m think of doing this (the below one).
Any idea for this:

Plotting Frequency band for each Channels to extract Information

Hi, The dataset contains recording of 14 channels and their event/class. There are totally 4 classes among which listening and resting are two. I want to predict the 4 classes based on the 14 EEG channels and see which frequency band influence the result more. And also mne.find_events only detects two classes and not sure which two classes they represent. Is there a way to do this? Thanks

Thanks

[richard (Richard Höchenberger)]

Hello,

@Star

Then it should be 0 ,1 and 2 events right

No, because 0 means “nothing” in a stim channel.

@Star

Yes it fluctuates so much that’s the reason I’m think of doing this (the below one).
Any idea for this:

I’m failing to see the connection here.

I was asking because normally, triggers (event codes) need to persist over several samples, whereas in your example, they seem to change with every row. The only explanation I have is that rows don’t represent samples here. But if they don’t, then using a RawArray for your data doesn’t make any sense.

Best wishes,
Richard

[Star (Arjun Rajan)]

Hello @richard,

Ok. Thanks.

Yes it does persist over several samples, I change it so that you can see different class in the screenshot. The next event occurs after a long time only.

The entire dataset actually contains Timestamp, 14-channels, Classification/Label/event, PPG, BPM, IBI etc. for each patients in separate .csv file.
What I want to do is to predict the Class/label based on the EEG signal and figure if there are any feature or frequency band that particular affects the classification?
And figure how the EEG signal looks when a person is listening, writing etc…
Am I going in a the wrong approach for this.
What the right approach then?

Thanks

[Star (Arjun Rajan)]

Hi richard,

Was trying to extract the features from EEG Signal. For that wanted to import the data and epoch it.

tmin, tmax = -0.2, 0.5
event_id = dict(listening=1, writing=3)

# Setup for reading the raw data
raw = mne.io.RawArray(data, info)  
raw.filter(.5, None, fir_design='firwin')
events = mne.read_events(data)
picks = mne.pick_types(raw.info, eeg=True)

# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks, proj=True,
                    baseline=None, preload=True)
labels = epochs.events[:, -1]

# get MEG and EEG data
data = epochs.get_data()

Had the data in csv format like before. Here for the read_events since I don’t have separate event/label file, I gave the data path itself which I converted from csv and transposed to numpy.ndarray.

Now when I run the code I get

TypeError: fname must be an instance of path-like, got <class ‘numpy.ndarray’> instead.

Don’t have a path to the event/label. I know I need to give fname of the events.
How to handle this error if I don’t have event_fname? How to resolve this?

Thanks