# Authors: Mainak Jas <[email protected]>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score, ShuffleSplit
from mne.decoding import Vectorizer, FilterEstimator
import mne
from mne.datasets import sample
from mne_realtime import MockRtClient, RtEpochs
print(__doc__)
# Fiff file to simulate the realtime client
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
tmin, tmax = -0.2, 0.5
event_id = dict(aud_l=1, vis_l=3)
tr_percent = 60 # Training percentage
min_trials = 10 # minimum trials after which decoding should start
# select gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=True, exclude=raw.info['bads'])
# create the mock-client object
rt_client = MockRtClient(raw)
# create the real-time epochs object
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks, decim=1,
reject=dict(grad=4000e-13, eog=150e-6), baseline=None,
isi_max=4.)
# start the acquisition
rt_epochs.start()
# send raw buffers
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=90, buffer_size=1000)
# Decoding in sensor space using a linear SVM
n_times = len(rt_epochs.times)
scores_x, scores, std_scores = [], [], []
# don't highpass filter because it's epoched data and the signal length
# is small
filt = FilterEstimator(rt_epochs.info, None, 40, fir_design='firwin')
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = LogisticRegression(solver='lbfgs')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info, meg='grad', eeg=False, eog=False,
stim=False, exclude=raw.info['bads'])
ax = plt.subplot(111)
ax.set_xlabel('Trials')
ax.set_ylabel('Classification score (% correct)')
ax.set_title('Real-time decoding')
ax.set_xlim([min_trials, 50])
ax.set_ylim([30, 105])
plt.axhline(50, color='k', linestyle='--', label="Chance level")
plt.show(block=False)
for ev_num, ev in enumerate(rt_epochs.iter_evoked()):
if ev_num >= 50: # stop at 50
break
print("Just got epoch %d" % (ev_num + 1))
if ev_num == 0:
X = ev.data[np.newaxis, data_picks, :]
y = int(ev.comment) # the comment attribute contains the event_id
else:
X = np.concatenate((X, ev.data[np.newaxis, data_picks, :]), axis=0)
y = np.append(y, int(ev.comment))
if ev_num >= min_trials and ev_num % 5 == 0:
cv = ShuffleSplit(5, test_size=0.2, random_state=42) # 3 for speed
scores_t = cross_val_score(concat_classifier, X, y, cv=cv,
n_jobs=1) * 100
std_scores.append(scores_t.std())
scores.append(scores_t.mean())
scores_x.append(ev_num)
# Plot accuracy
plt.plot(scores_x[-2:], scores[-2:], '-x', color='b',
label="Classif. score")
ax.plot(scores_x[-1], scores[-1])
hyp_limits = (np.asarray(scores) - np.asarray(std_scores),
np.asarray(scores) + np.asarray(std_scores))
fill = plt.fill_between(scores_x, hyp_limits[0], y2=hyp_limits[1],
color='b', alpha=0.5)
plt.pause(0.01)
plt.draw()
ax.collections.remove(fill) # Remove old fill area
plt.fill_between(scores_x, hyp_limits[0], y2=hyp_limits[1], color='b',
alpha=0.5)
plt.draw() # Final figureNone
Opening raw data file C:\Users\mne_data\MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
Not setting metadata
No baseline correction applied
4 projection items activated
Traceback (most recent call last):
File "C:/Users/Documents/test.py", line 40, in <module>
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks, decim=1,
File "<decorator-gen-469>", line 21, in __init__
File "C:\Users\anaconda3\lib\site-packages\mne_realtime\epochs.py", line 155, in __init__
super(RtEpochs, self).__init__(
File "<decorator-gen-190>", line 21, in __init__
File "C:\Users\anaconda3\lib\site-packages\mne\epochs.py", line 547, in __init__
self._check_consistency()
File "C:\Users\anaconda3\lib\site-packages\mne\epochs.py", line 557, in _check_consistency
assert isinstance(self.drop_log, tuple)
AssertionError
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