Parsing EDF dataset to .MAT (matlab file structure)
I'm trying to parse an EDF dataset to the .MAT file structure described below (originally from https://www.bbci.de/competition/iv/desc_1.html): dict_keys(['header', 'version', 'globals', 'mrk', 'cnt', 'nfo'])
The new .MAT file (i.e. Matlab format *.mat) must contain a set variables, and so far, I was able to convert the following:
sample_rate = raw.info["sfreq"]
nchannels=len(raw.ch_names)
nsamples=raw.n_times
raw.rename_channels({ch: ch.replace('.', '') for ch in raw.ch_names})
channel_names = raw.ch_names
labels = np.zeros((1, nsamples), int)
labels[0, event_onsets] = event_codes
cl_lab = ['left', 'right']
nclasses = len(cl_lab)
nevents = len(event_onsets)
now, I must parse the continuous EEG signals, size [time x channels]:
EEG = m['cnt'].T # Numpy array within the continuous EEG signals, size [time x channels]
EEG = m['cnt'].T.shape
(59, 190473)
which looks like the array of data below:
array([[ -36, -203, -384, ..., -256, -287, -289],
[ -138, -774, -1463, ..., -262, -243, -158],
[ -50, -280, -517, ..., 13, -8, -2],
...,
[ -58, -308, -532, ..., -136, -148, -123],
[ -81, -438, -787, ..., -185, -204, -195],
[ -270, -1479, -2709, ..., 106, 193, 322]], dtype=int16)
Plus, I need to parse 2 more event related variables: event_onsets and event_codes
They are:
event_onsets = m['mrk'][0][0][0]
array([[ 2095, 2895, 3695, 4495, 5295, 6095, 6895, 7695,
8495, 9295, 10095, 10895, 11695, 12495, 13295, 16294,
17094, 17894, 18694, 19494, 20294, 21094, 21894, 22694,
23494, 24295, 25095, 25895, 26695, 27495, 30494, 31294,
32094, 32894, 33694, 34494, 35294, 36094, 36894, 37694,
38494, 39294, 40094, 40894, 41694, 44693, 45493, 46293,
47093, 47893, 48693, 49493, 50293, 51093, 51893, 52693,
53493, 54293, 55093, 55893, 58892, 59692, 60492, 61292,
62092, 62892, 63692, 64492, 65292, 66093, 66893, 67693,
68493, 69293, 70093, 73092, 73892, 74692, 75492, 76292,
77092, 77892, 78692, 79492, 80292, 81092, 81892, 82692,
83492, 84292, 87291, 88091, 88891, 89691, 90491, 91291,
92091, 92891, 93691, 94491, 97292, 98092, 98892, 99692,
100492, 101292, 102092, 102892, 103692, 104492, 105292, 106092,
106892, 107692, 108492, 111491, 112291, 113091, 113891, 114691,
115491, 116291, 117091, 117891, 118691, 119492, 120292, 121091,
121891, 122692, 125691, 126491, 127291, 128091, 128891, 129691,
130491, 131291, 132091, 132891, 133691, 134491, 135291, 136091,
136891, 139890, 140690, 141490, 142290, 143090, 143890, 144690,
145490, 146290, 147090, 147890, 148690, 149490, 150290, 151090,
154089, 154889, 155689, 156489, 157289, 158090, 158890, 159690,
160490, 161290, 162090, 162890, 163690, 164490, 165290, 168289,
169089, 169889, 170689, 171489, 172289, 173089, 173889, 174689,
175489, 176289, 177089, 177890, 178689, 179489, 182488, 183288,
184088, 184888, 185688, 186488, 187288, 188088, 188888, 189688]],
dtype=int32)
event_onsets.shape
(1, 200)
and:
event_codes = m['mrk'][0][0][1]
array([[ 1, 1, 1, -1, 1, 1, 1, -1, 1, 1, 1, -1, 1, -1, -1, -1,
1, -1, -1, -1, 1, 1, 1, -1, 1, -1, -1, -1, 1, -1, 1, 1,
1, -1, 1, 1, -1, -1, -1, -1, 1, -1, 1, 1, 1, 1, 1, -1,
-1, -1, 1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, -1, -1, 1,
1, 1, 1, -1, -1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1,
1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, -1, 1, -1, -1, 1,
-1, -1, -1, -1, -1, -1, -1, -1, 1, -1, -1, 1, 1, -1, -1, -1,
1, 1, 1, 1, -1, -1, 1, -1, 1, -1, -1, 1, 1, 1, 1, 1,
-1, 1, 1, -1, 1, -1, -1, 1, -1, 1, -1, -1, -1, -1, 1, 1,
-1, 1, -1, 1, -1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1,
1, 1, 1, 1, -1, 1, -1, -1, 1, -1, -1, 1, -1, 1, 1, 1,
-1, 1, 1, -1, -1, 1, 1, -1, 1, 1, -1, -1, -1, 1, 1, 1,
1, 1, -1, -1, 1, 1, -1, -1]], dtype=int16)
event_codes.shape
(1, 200)
Thus, where are the data (in the EDF dataset) corresponding to ['cnt'] and ['mkr'] (.MAT) structures?
['cnt'].T # Numpy array within the continuous EEG signals, size [time x channels] ['mrk']: structure of target cue information with fields
Best wishes,
In fact, file extension doesn’t even matter at all. The important part was parsing data (from the EDF file) to the correct variables: event_codes and event_onsets.
Then, it’d be possible to run a few statistical and CNN algorithms (PSD and LDA).
The key documentation is https://mne.tools/stable/glossary.html#term-events
So, It was possible to create [event_onsets] variable with the 1st column of [events], as well as [event_codes] with the 3rd column of [events].