Hi everyone,
I want to load BCI competition IV dataset 2a on colab. In the description of dataset, it says “One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.”
According to that, there must be 72 trials for each of the four classes in each session.
However, when I load it, the class distributions are unbalanced for session A06E. It shows that all trials belongs to class 1. Is there anything I am wrong? My code is following:
> filename = "/content/drive/MyDrive/Colab_Notebooks/A06E.gdf"
> raw = mne.io.read_raw_gdf(filename)
> events, _ = mne.events_from_annotations(raw)
> picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=False, stim=False, exclude='bads')
> tmin, tmax = 1., 4.
> # left_hand = 769,right_hand = 770,foot = 771,tongue = 772
> event_id = dict({'769': 7,'770': 8,'771': 9,'772': 10})
> epochs = mne.Epochs(raw, events,event_id, tmin, tmax, proj=True, picks=picks,baseline=None, preload=True, on_missing='warn')
and
print(np.unique(events[:,2], return_counts=True))
it prints:
(array([1, 2, 3, 4, 5, 6, 7]), array([ 73, 1, 1, 1, 9, 288, 288]))
Here 7 is for left hand, and there is nothing about right hand(8), foot(9) and tongue(10).
Thank you.