# ERP XDAWN Decoding

**URL:** <https://mne.discourse.group/t/erp-xdawn-decoding/5919>\
**Category:** Support & Discussions\
**Tags:** eeglab, data-import\
**Created:** [November 14, 2022, 1:59pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919 "2022-11-14T13:59:26Z")\
**Posts on this page:** 14\
**Page:** 1

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 14, 2022, 1:59pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/1 "2022-11-14T13:59:26Z")

</div>

Dear MNE experts:

I have one issue while computing an XDAWN. I have tried XDAWN on ERP datasets. I thought the problem is due to gbk encoding. But I can not add it in the fname = os.path.join(eeg\_path,‘010101\_1.set’).  
May I ask how to solve this ‘gbk’ problem?

Best wishes  
YuTong`

```python
type or paste code here
import mne
import os
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import MinMaxScaler

from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info
from mne.preprocessing import Xdawn
from mne.decoding import Vectorizer

print( __doc__ )

data_path = data_path = 'C:\WM_DATA\DATA\DATA'
eeg_path = data_path 
fname = os.path.join(eeg_path,'010101_1.set')
tmin, tmax = -0.1, 0.3
event_id = {'B9(size4_left_noc)/71': 46, 'B5(size2_left_noc)/61': 55, 'B3(size1_right_noc)/53': 56, 'B2(size1_left_c)/52': 59, 'B1(size1_left_noc)/51': 59, 'B8(size2_right_c)/64': 48, 'B4(size1_right_c)/54': 56, 'B6(size2_left_c)/62': 56, 'B12(size4_right_c)/74': 19, 'B10(size4_left_c)/72': 49}
n_filter = 3

# Setup for reading the raw data
raw = mne.io.read_epochs_eeglab(fname)
raw.filter(1, 20, fir_design='firwin')
events = read_events(fname)

picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                   exclude='bads')

epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                picks=picks, baseline=None, preload=True,
                verbose=False)

# Create classification pipeline
clf = make_pipeline(Xdawn(n_components=n_filter),
                    Vectorizer(),
                    MinMaxScaler(),
                    LogisticRegression(penalty='l1', solver='liblinear',
                                       multi_class='auto'))

# Get the labels
labels = epochs.events[:, -1]

# Cross validator
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)

# Do cross-validation
preds = np.empty(len(labels))
for train, test in cv.split(epochs, labels):
    clf.fit(epochs[train], labels[train])
    preds[test] = clf.predict(epochs[test])

# Classification report
target_names = ['aud_l', 'aud_r', 'vis_l', 'vis_r']
report = classification_report(labels, preds, target_names=target_names)
print(report)

# Normalized confusion matrix
cm = confusion_matrix(labels, preds)
cm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]

# Plot confusion matrix
fig, ax = plt.subplots(1)
im = ax.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)
ax.set(title='Normalized Confusion matrix')
fig.colorbar(im)
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
fig.tight_layout()
ax.set(ylabel='True label', xlabel='Predicted label')

```

Module created for script run in IPython  
Extracting parameters from C:\WM\_DATA\DATA\DATA\010101\_1.set…  
Not setting metadata  
613 matching events found  
c:\users\yyt.spyder-py3\xdawn.py:27: RuntimeWarning: At least one epoch has multiple events. Only the latency of the first event will be retained.  
raw = mne.io.read\_epochs\_eeglab(fname)  
C:\Users\yyt\mne-python\1.1.1\_0\lib\site-packages\mne\io\eeglab\eeglab.py:149: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.  
if d.get(“type”, None) != ‘FID’:  
C:\Users\yyt\mne-python\1.1.1\_0\lib\site-packages\mne\io\eeglab\eeglab.py:149: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.  
if d.get(“type”, None) != ‘FID’:  
No baseline correction applied  
0 projection items activated  
Ready.  
Setting up band-pass filter from 1 - 20 Hz

## FIR filter parameters

Designing a one-pass, zero-phase, non-causal bandpass filter:

- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 20.00 Hz
- Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 22.50 Hz)
- Filter length: 3301 samples (3.301 sec)

c:\users\yyt.spyder-py3\xdawn.py:28: RuntimeWarning: filter\_length (3301) is longer than the signal (1400), distortion is likely. Reduce filter length or filter a longer signal.  
raw.filter(1, 20, fir\_design=‘firwin’)  
[Parallel(n\_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.  
[Parallel(n\_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s  
[Parallel(n\_jobs=1)]: Done 2 out of 2 | elapsed: 0.0s remaining: 0.0s  
[Parallel(n\_jobs=1)]: Done 3 out of 3 | elapsed: 0.0s remaining: 0.0s  
[Parallel(n\_jobs=1)]: Done 4 out of 4 | elapsed: 0.0s remaining: 0.0s  
[Parallel(n\_jobs=1)]: Done 17777 out of 17777 | elapsed: 7.4s finished  
c:\users\yyt.spyder-py3\xdawn.py:29: RuntimeWarning: This filename (C:\WM\_DATA\DATA\DATA\010101\_1.set) does not conform to MNE naming conventions. All events files should end with .eve, -eve.fif, -eve.fif.gz, -eve.lst, -eve.txt, \_eve.fif, \_eve.fif.gz, \_eve.lst, \_eve.txt or -annot.fif  
events = read\_events(fname)  
Traceback (most recent call last):

File “C:\Users\yyt\mne-python\1.1.1\_0\lib\site-packages\spyder\_kernels\py3compat.py”, line 356, in compat\_exec  
exec(code, globals, locals)

File “c:\users\yyt.spyder-py3\xdawn.py”, line 29, in   
events = read\_events(fname)

File “”, line 12, in read\_events

File “C:\Users\yyt\mne-python\1.1.1\_0\lib\site-packages\mne\event.py”, line 266, in read\_events  
lines = np.loadtxt(filename, dtype=np.float64).astype(int)

File “C:\Users\yyt\mne-python\1.1.1\_0\lib\site-packages\numpy\lib\npyio.py”, line 1098, in loadtxt  
first\_line = next(fh)

UnicodeDecodeError: ‘gbk’ codec can’t decode byte 0xca in position 188: illegal multibyte sequence

---

<div class="post-metadata">

**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:** [November 14, 2022, 2:54pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/2 "2022-11-14T14:54:20Z")

</div>

Hello @hp20072929 and welcome to the forum!

> [@hp20072929](#):
>
> ```python
> fname = os.path.join(eeg_path,'010101_1.set')
> ...
> # Setup for reading the raw data
> raw = mne.io.read_epochs_eeglab(fname)
> raw.filter(1, 20, fir_design='firwin')
> events = read_events(fname)
> 
> ```

`read_events()` cannot work with a `.set` file.

The EEGLAB reader automatically [creates](https://mne.tools/dev/auto_tutorials/intro/20_events_from_raw.html#reading-embedded-events-as-annotations) annotations, which can be [converted](https://mne.tools/dev/auto_tutorials/intro/20_events_from_raw.html#converting-between-events-arrays-and-annotations-objects) to events:

```python
raw = mne.io.read_raw_eeglab(fname)
events, event_id = mne.events_from_annotations(raw)

```

I’m also not sure why you’re using `read_epochs_eeglab()` to read raw data? I’m surprised it even works…

Best wishes,  
Richard

---

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 15, 2022, 8:28am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/3 "2022-11-15T08:28:04Z")

</div>

Thank you so much, Richard.  
After changing, I have a new problem with mat file. And may I ask if would it be possible to run XDAWN on Event-related potential dataset?

Best regards  
YuTong

```python
import mne
import os
import numpy as np
import matplotlib.pyplot as plt

from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import MinMaxScaler

from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info
from mne.preprocessing import Xdawn
from mne.decoding import Vectorizer

print( __doc__ )

data_path = data_path = 'C:\WM_DATA\\raw'
eeg_path = data_path 
fname = os.path.join(eeg_path,'010101_1.vhdr')
tmin, tmax = -0.1, 0.3
event_id = {'B9(size4_left_noc)/71': 46, 'B5(size2_left_noc)/61': 55, 'B3(size1_right_noc)/53': 56, 'B2(size1_left_c)/52': 59, 'B1(size1_left_noc)/51': 59, 'B8(size2_right_c)/64': 48, 'B4(size1_right_c)/54': 56, 'B6(size2_left_c)/62': 56, 'B12(size4_right_c)/74': 19, 'B10(size4_left_c)/72': 49}
n_filter = 3

# Setup for reading the raw data
raw = mne.io.read_raw_eeglab(fname)

events, event_id = mne.events_from_annotations(raw)

picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                   exclude='bads')

epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                picks=picks, baseline=None, preload=True,
                verbose=False)

# Create classification pipeline
clf = make_pipeline(Xdawn(n_components=n_filter),
                    Vectorizer(),
                    MinMaxScaler(),
                    LogisticRegression(penalty='l1', solver='liblinear',
                                       multi_class='auto'))

# Get the labels
labels = epochs.events[:, -1]

# Cross validator
cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)

# Do cross-validation
preds = np.empty(len(labels))
for train, test in cv.split(epochs, labels):
    clf.fit(epochs[train], labels[train])
    preds[test] = clf.predict(epochs[test])

# Classification report
target_names = ['aud_l', 'aud_r', 'vis_l', 'vis_r']
report = classification_report(labels, preds, target_names=target_names)
print(report)

# Normalized confusion matrix
cm = confusion_matrix(labels, preds)
cm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]

# Plot confusion matrix
fig, ax = plt.subplots(1)
im = ax.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)
ax.set(title='Normalized Confusion matrix')
fig.colorbar(im)
tick_marks = np.arange(len(target_names))
plt.xticks(tick_marks, target_names, rotation=45)
plt.yticks(tick_marks, target_names)
fig.tight_layout()
ax.set(ylabel='True label', xlabel='Predicted label')

```

```python
runfile('C:/Users/yyt/.spyder-py3/11_15.py', wdir='C:/Users/yyt/.spyder-py3')
Module created for script run in IPython
Traceback (most recent call last):

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec
    exec(code, globals, locals)

  File "c:\users\yyt\.spyder-py3\11_15.py", line 27, in <module>
    raw = mne.io.read_raw_eeglab(fname)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 259, in read_raw_eeglab
    return RawEEGLAB(input_fname=input_fname, preload=preload,

  File "<decorator-gen-277>", line 12, in __init__

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 358, in __init__
    eeg = _check_load_mat(input_fname, uint16_codec)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 60, in _check_load_mat
    eeg = _readmat(fname, uint16_codec=uint16_codec)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\_eeglab.py", line 82, in _readmat
    return read_mat(fname, uint16_codec=uint16_codec)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\pymatreader\pymatreader.py", line 87, in read_mat
    mjv, _ = matfile_version(fid)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\scipy\io\matlab\_miobase.py", line 223, in matfile_version
    return _get_matfile_version(fileobj)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\scipy\io\matlab\_miobase.py", line 251, in _get_matfile_version
    raise ValueError('Unknown mat file type, version %s, %s' % ret)

ValueError: Unknown mat file type, version 10, 68

```

---

<div class="post-metadata">

**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:** [November 15, 2022, 9:54am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/4 "2022-11-15T09:54:24Z")

</div>

The file you’re trying to load ends with .vhdr. This is a BrainVision, not an EEGLAB file.

---

<div class="post-metadata">

**Author:** ![alexsunny123](https://avatars.discourse-cdn.com/v4/letter/a/51bf81/32.png) [@alexsunny123](https://mne.discourse.group/u/alexsunny123)\
**Post date:** [November 15, 2022, 11:33am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/5 "2022-11-15T11:33:51Z")

</div>

> [@hp20072929](#):
>
> Thank you so much, Richard.  
> After changing, I have a new problem with mat file. And may I ask if would it be possible to run XDAWN on Event-related potential dataset?
> 
> Best regards  
> YuTong
> 
> ```python
> import mne
> import os
> import numpy as np
> import matplotlib.pyplot as plt
> 
> from sklearn.model_selection import StratifiedKFold
> from sklearn.pipeline import make_pipeline
> from sklearn.linear_model import LogisticRegression
> from sklearn.metrics import classification_report, confusion_matrix
> from sklearn.preprocessing import MinMaxScaler
> 
> from mne import io, pick_types, read_events, Epochs, EvokedArray, create_info
> from mne.preprocessing import Xdawn
> from mne.decoding import Vectorizer
> 
> print( __doc__ )
> 
> data_path = data_path = 'C:\WM_DATA\\raw'
> eeg_path = data_path 
> fname = os.path.join(eeg_path,'010101_1.vhdr')
> tmin, tmax = -0.1, 0.3
> event_id = {'B9(size4_left_noc)/71': 46, 'B5(size2_left_noc)/61': 55, 'B3(size1_right_noc)/53': 56, 'B2(size1_left_c)/52': 59, 'B1(size1_left_noc)/51': 59, 'B8(size2_right_c)/64': 48, 'B4(size1_right_c)/54': 56, 'B6(size2_left_c)/62': 56, 'B12(size4_right_c)/74': 19, 'B10(size4_left_c)/72': 49}
> n_filter = 3
> 
> # Setup for reading the raw data
> raw = mne.io.read_raw_eeglab(fname)
> 
> events, event_id = mne.events_from_annotations(raw)
> 
> picks = pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
> exclude='bads')
> 
> epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
> picks=picks, baseline=None, preload=True,
> verbose=False)
> 
> # Create classification pipeline
> clf = make_pipeline(Xdawn(n_components=n_filter),
> Vectorizer(),
> MinMaxScaler(),
> LogisticRegression(penalty='l1', solver='liblinear',
> multi_class='auto'))
> 
> # Get the labels
> labels = epochs.events[:, -1]
> 
> # Cross validator
> cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)
> 
> # Do cross-validation
> preds = np.empty(len(labels))
> for train, test in cv.split(epochs, labels):
> clf.fit(epochs[train], labels[train])
> preds[test] = clf.predict(epochs[test])
> 
> # Classification report
> target_names = ['aud_l', 'aud_r', 'vis_l', 'vis_r']
> report = classification_report(labels, preds, target_names=target_names)
> print(report)
> 
> # Normalized confusion matrix
> cm = confusion_matrix(labels, preds)
> cm_normalized = cm.astype(float) / cm.sum(axis=1)[:, np.newaxis]
> 
> # Plot confusion matrix
> fig, ax = plt.subplots(1)
> im = ax.imshow(cm_normalized, interpolation='nearest', cmap=plt.cm.Blues)
> ax.set(title='Normalized Confusion matrix')
> fig.colorbar(im)
> tick_marks = np.arange(len(target_names))
> plt.xticks(tick_marks, target_names, rotation=45)
> plt.yticks(tick_marks, target_names)
> fig.tight_layout()
> ax.set(ylabel='True label', xlabel='Predicted label')
> 
> ```
> 
> ```python
> runfile('C:/Users/yyt/.spyder-py3/11_15.py', wdir='C:/Users/yyt/.spyder-py3')
> Module created for script run in IPython
> Traceback (most recent call last):
> 
> File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec
> exec(code, globals, locals)
> 
> File "c:\users\yyt\.spyder-py3\11_15.py", line 27, in <module>
> raw = mne.io.read_raw_eeglab(fname)
> 
> File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 259, in read_raw_eeglab
> return RawEEGLAB(input_fname=input_fname, preload=preload,
> 
> File "<decorator-gen-277>", line 12, in __init__
> 
> File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 358, in __init__
> eeg = _check_load_mat(input_fname, uint16_codec)
> 
> File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\eeglab.py", line 60, in _check_load_mat
> eeg = _readmat(fname, uint16_codec=uint16_codec)
> 
> File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\io\eeglab\_eeglab.py", line 82, in _readmat
> return read_mat(fname, uint16_codec=uint16_codec)
> 
> ```

thanks for the awesome information.

---

<div class="post-metadata">

**Author:** ![mscheltienne](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/mscheltienne/32/827_2.png) [@mscheltienne](https://mne.discourse.group/u/mscheltienne)\
**Post date:** [November 15, 2022, 12:53pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/6 "2022-11-15T12:53:48Z")

</div>

You are still trying to load a BrainVision file (`fname = os.path.join(eeg_path,'010101_1.vhdr')`) with the EEGLab reader `raw = mne.io.read_raw_eeglab(fame)`, which can not work. Please read it with the correct reader, in this case: `raw = mne.io.read_raw_brainvision(fname)` or with the general reader `raw = mne.io.read_raw(fname)` which infers the data format from the file extension.

---

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 16, 2022, 1:11pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/7 "2022-11-16T13:11:53Z")

</div>

Thanks so much.  
After solving the format.  
I went into a problem: MemoryError: Unable to allocate 9.88 GiB for an array with shape (401, 3307889) and data type float64

```python
runfile('C:/Users/yyt/.spyder-py3/11_15.py', wdir='C:/Users/yyt/.spyder-py3')
Module created for script run in IPython
Reading C:\WM_DATA\raw_set\01_1.fdt
Used Annotations descriptions: ['S 5', 'S 6', 'S 11', 'S 12', 'S 21', 'S 22', 'S 23', 'S 24', 'S 31', 'S 32', 'S 33', 'S 34', 'S 41', 'S 42', 'S 43', 'S 44', 'S 51', 'S 52', 'S 53', 'S 54', 'S 61', 'S 62', 'S 63', 'S 64', 'S 71', 'S 72', 'S 73', 'S 74', 'S 77', 'S 88', 'S 99', 'boundary']
c:\users\yyt\.spyder-py3\11_15.py:27: RuntimeWarning: The data contains 'boundary' events, indicating data discontinuities. Be cautious of filtering and epoching around these events.
  raw = mne.io.read_raw_eeglab(fname)
C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=10.
  warnings.warn(
Computing rank from data with rank='full'
    EEG: rank 31 from info
Reducing data rank from 31 -> 31
Estimating covariance using EMPIRICAL
Done.
Traceback (most recent call last):

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec
    exec(code, globals, locals)

  File "c:\users\yyt\.spyder-py3\11_15.py", line 54, in <module>
    clf.fit(epochs[train], labels[train])

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\sklearn\pipeline.py", line 378, in fit
    Xt = self._fit(X, y, **fit_params_steps)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\sklearn\pipeline.py", line 336, in _fit
    X, fitted_transformer = fit_transform_one_cached(

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\joblib\memory.py", line 349, in __call__
    return self.func(*args, **kwargs)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\sklearn\pipeline.py", line 870, in _fit_transform_one
    res = transformer.fit_transform(X, y, **fit_params)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\decoding\mixin.py", line 33, in fit_transform
    return self.fit(X, y, **fit_params).transform(X)

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\preprocessing\xdawn.py", line 460, in fit
    filters, patterns, evokeds = _fit_xdawn(

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\preprocessing\xdawn.py", line 171, in _fit_xdawn
    evokeds, toeplitzs = _least_square_evoked(

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\mne\preprocessing\xdawn.py", line 87, in _least_square_evoked
    toeplitz.append(linalg.toeplitz(trig[0:window], trig))

  File "C:\Users\yyt\mne-python\1.1.1_0\lib\site-packages\scipy\linalg\_special_matrices.py", line 199, in toeplitz
    return as_strided(vals[len(c)-1:], shape=out_shp, strides=(-n, n)).copy()

MemoryError: Unable to allocate 9.88 GiB for an array with shape (401, 3307889) and data type float64

```

Best regards  
YuTong

---

<div class="post-metadata">

**Author:** ![mscheltienne](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/mscheltienne/32/827_2.png) [@mscheltienne](https://mne.discourse.group/u/mscheltienne)\
**Post date:** [November 16, 2022, 1:20pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/8 "2022-11-16T13:20:50Z")

</div>

The error is self-explanatory. You ran out of RAM thus it could not allocate 9.88 Gb of RAM for this operation. Either you optimize your code/RAM usage to decrease the memory consumption (for instance making sure you don’t store large arrays you don’t use in a variable), or you run your code on a computer with more RAM.

---

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 17, 2022, 7:50am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/9 "2022-11-17T07:50:50Z")

</div>

Thanks.  
In fact, this computer has 16GB RAM.  
I have two ideas:

1. Modify virtual memory (not working)
2. Numpy uses lower precision when defining arrays. Reduced from float64 to float32.  
My concern is whether would this affect the results a lot.  
Best wishes  
YuTong

---

<div class="post-metadata">

**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:** [November 17, 2022, 8:53am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/10 "2022-11-17T08:53:58Z")

</div>

You should just use a computer with more RAM or use less data. Anything else will just eat up much of your time without a promise of success.

Best wishes,  
Richard

---

<div class="post-metadata">

**Author:** ![drammock](https://yyz2.discourse-cdn.com/free1/user_avatar/mne.discourse.group/drammock/32/4_2.png) [@drammock](https://mne.discourse.group/u/drammock)\
**Post date:** [November 17, 2022, 3:00pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/11 "2022-11-17T15:00:06Z")

</div>

there is also a `preload=False` option to `read_raw_brainvision()`. It allows you to load the raw metadata, then crop to something shorter (or perform epoching), then load the data samples.

---

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 18, 2022, 10:39am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/12 "2022-11-18T10:39:24Z")

</div>

Thanks, everyone.  
It is working perfectly.  
Best wishes

---

<div class="post-metadata">

**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:** [November 18, 2022, 11:14am UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/13 "2022-11-18T11:14:38Z")

</div>

What did you do to fix it?

---

<div class="post-metadata">

**Author:** ![hp20072929](https://avatars.discourse-cdn.com/v4/letter/h/3bc359/32.png) [@hp20072929](https://mne.discourse.group/u/hp20072929)\
**Post date:** [November 18, 2022, 3:27pm UTC](https://mne.discourse.group/t/erp-xdawn-decoding/5919/14 "2022-11-18T15:27:40Z")

</div>

Generally, I re-run the preprocessing.