Parsing EDF dataset to .MAT (matlab file structure)

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1

I’m trying to parse an EDF dataset to the .MAT file structure described below
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,

2

Hello,

I suppose this would be raw.get_data().T
As for the mkr thing, no idea.

3

Are you converting to .mat format because you want to load it with MATLAB? If so, there are EDF readers for MATLAB already. A quick internet search of matlab eeg read edf returned many hits.

4

No.
In fact, file extension doesn’t even matter at all. The important part is to parse and understand the correct 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).

I tried to use:

events=mne.events_from_annotations(raw)

But “events” array, turned to be very different in dimension/shape. So that it confused me.

events[0].shape
(30, 3)

while:

event_onsets.shape
    (1, 200)
event_codes.shape
    (1, 200)

[event_codes] is an array of -1 and 1, meaning “left” and “right” to classify the correspondent value located in the same position of array [event_onsets]. That explains the reason they have the same dimensions:

So, [events] (from mne.events_from_annotations) and [event_codes]/[event_onsets] have very different dimensions/shapes. However, my intuition tells me that [event_codes] and [event_onsets] still can be extracted from [events] (i.e. mne.events_from_annotations(raw)):

events=mne.events_from_annotations(raw)
events[0].shape
(30, 2)
array([[  656,     0,     2],
       [ 1968,     0,     3],
       [ 3280,     0,     3],
       [ 4592,     0,     2],
       [ 5904,     0,     2],
       [ 7216,     0,     3],
       [ 8528,     0,     3],
       [ 9840,     0,     2],
       [11152,     0,     3],
       [12464,     0,     2],
       [13776,     0,     2],
       [15088,     0,     3],
       [16400,     0,     2],
       [17712,     0,     3]])

So, It’s possible to create [event_onsets] variable with the 1st column of [events], as well as [event_codes] with the 3rd column of [events].

How could I generate them, making sure I won’t be missing/losing/mishandling data ?

But that’s just my guess/intuition, and that’s why I posted this thread.

What do you suggest/guess? :wink:

Best wishes,
I

5

Thanks Richard,
I’ve amended my script and it seems to work.
I’ve just answered @drammock with enlightening ideas I had, regarding event_onsets and event_codes

6

Please see our glossary entry here

https://mne.tools/stable/glossary.html#term-events

7

Right on! Thanks for the documentation
I was able to convert EDF data and ran CNN and LDA algorithms successfully.
EEG_BCI200_Motor_Imagery_Analysis_Classification_Dataset_1_EDF.
Best wishes,
I

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