def xdawn_embedding(data, use_xdawn):
"""Perform embedding of EEG data in 2D Euclidean space
with Laplacian Eigenmaps.
Parameters
----------
data : dict
A dictionary containing training and testing data
Returns
-------
array
Embedded
"""
if use_xdawn:
nfilter = 3
xdwn = XdawnCovariances(estimator='scm', nfilter=nfilter)
covs = xdwn.fit(data['train_x'],
data['train_y']).transform(data['test_x'])
lapl = Embedding(metric='riemann', n_components=3)
embd = lapl.fit_transform(covs)
else:
tangent_space = Pipeline([
('cov_transform', Covariances(estimator='lwf')),
('tangent_space', TangentSpace(metric='riemann'))
])
t_space = tangent_space.fit(data['train_x'],
data['train_y']).transform(data['test_x'])
reducer = umap.UMAP(n_neighbors=30, min_dist=1, spread=2)
embd = reducer.fit_transform(t_space)
return embd
def get_sourcetarget_split_p300(source, target, ncovs_train):
X_source = source['signals']
y_source = source['labels'].flatten()
covs_source = XdawnCovariances(classes=[2]).fit_transform(
X_source, y_source)
source = {}
source['covs'] = covs_source
source['labels'] = y_source
X_target = target['signals']
y_target = target['labels'].flatten()
if ncovs_train is None:
ncovs_train = np.sum(y_target == 2)
sel = np.arange(len(y_target))
np.random.shuffle(sel)
X_target = X_target[sel]
y_target = y_target[sel]
idx_erps = np.where(y_target == 2)[0][:ncovs_train]
idx_rest = np.where(
y_target == 1)[0][:ncovs_train *
5] # because there's one ERP in every 6 flashes
idx_train = np.concatenate([idx_erps, idx_rest])
idx_test = np.array(
[i for i in range(len(y_target)) if i not in idx_train])
erp = XdawnCovariances(classes=[2])
erp.fit(X_target[idx_train], y_target[idx_train])
target_train = {}
covs_target_train = erp.transform(X_target[idx_train])
y_target_train = y_target[idx_train]
target_train['covs'] = covs_target_train
target_train['labels'] = y_target_train
target_test = {}
covs_target_test = erp.transform(X_target[idx_test])
y_target_test = y_target[idx_test]
target_test['covs'] = covs_target_test
target_test['labels'] = y_target_test
return source, target_train, target_test
def xdawn_embedding(data):
"""Perform embedding of EEG data in 2D Euclidean space
with Laplacian Eigenmaps.
Parameters
----------
data : dict
A dictionary containing training and testing data
Returns
-------
array
Embedded
"""
nfilter = 3
xdwn = XdawnCovariances(estimator='scm', nfilter=nfilter)
covs = xdwn.fit(data['train_x'], data['train_y']).transform(data['test_x'])
lapl = Embedding(metric='riemann', n_components=3)
embd = lapl.fit_transform(covs)
return embd
print('Multiclass classification with XDAWN + MDM')
clf = make_pipeline(XdawnCovariances(n_components), MDM())
for train_idx, test_idx in cv.split(epochs_data):
y_train, y_test = labels[train_idx], labels[test_idx]
clf.fit(epochs_data[train_idx], y_train)
pr[test_idx] = clf.predict(epochs_data[test_idx])
print(classification_report(labels, pr))
###############################################################################
# plot the spatial patterns
xd = XdawnCovariances(n_components)
xd.fit(epochs_data, labels)
evoked.data = xd.Xd_.patterns_.T
evoked.times = np.arange(evoked.data.shape[0])
evoked.plot_topomap(
times=[0, n_components, 2 * n_components, 3 * n_components],
ch_type='grad',
colorbar=False,
size=1.5)
###############################################################################
# plot the confusion matrix
names = ['audio left', 'audio right', 'vis left', 'vis right']
plot_confusion_matrix(labels, pr, names)
plt.show()
epochs_data = epochs.get_data()
print("Multiclass classification with XDAWN + MDM")
clf = make_pipeline(XdawnCovariances(n_components), MDM())
for train_idx, test_idx in cv:
y_train, y_test = labels[train_idx], labels[test_idx]
clf.fit(epochs_data[train_idx], y_train)
pr[test_idx] = clf.predict(epochs_data[test_idx])
print(classification_report(labels, pr))
###############################################################################
# plot the spatial patterns
xd = XdawnCovariances(n_components)
xd.fit(epochs_data, labels)
evoked.data = xd.Xd.patterns_.T
evoked.times = np.arange(evoked.data.shape[0])
evoked.plot_topomap(
times=[0, n_components, 2 * n_components, 3 * n_components], ch_type="grad", colorbar=False, size=1.5
)
###############################################################################
# plot the confusion matrix
names = ["audio left", "audio right", "vis left", "vis right"]
plot_confusion_matrix(labels, pr, names)
plt.show()
# Read epochs
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
picks=picks, baseline=None, preload=True, verbose=False)
X = epochs.get_data()
y = epochs.events[:, -1]
###############################################################################
# Embedding the Xdawn covariance matrices with Laplacian Eigenmaps
nfilter = 4
xdwn = XdawnCovariances(estimator='scm', nfilter=nfilter)
split = train_test_split(X, y, train_size=0.25, random_state=42)
Xtrain, Xtest, ytrain, ytest = split
covs = xdwn.fit(Xtrain, ytrain).transform(Xtest)
lapl = Embedding(metric='riemann', n_components=2)
embd = lapl.fit_transform(covs)
###############################################################################
# Plot the three first components of the embedded points
fig, ax = plt.subplots(figsize=(7, 8), facecolor='white')
for cond, label in event_id.items():
idx = (ytest == label)
ax.scatter(embd[idx, 0], embd[idx, 1], s=36, label=cond)
ax.set_xlabel(r'$\varphi_1$', fontsize=16)
ax.set_ylabel(r'$\varphi_2$', fontsize=16)
X, y, meta = paradigm.get_data(datasets[condition], subjects=[subject])
y = LabelEncoder().fit_transform(y)
data[condition]['X'] = X
data[condition]['y'] = y
# estimate xDawn covs
ncomps = 4
erp = XdawnCovariances(classes=[1],
estimator='lwf',
nfilter=ncomps,
xdawn_estimator='lwf')
#erp = ERPCovariances(classes=[1], estimator='lwf', svd=ncomps)
split = train_test_split(X, y, train_size=0.50, random_state=42)
Xtrain, Xtest, ytrain, ytest = split
covs = erp.fit(Xtrain, ytrain).transform(Xtest)
Mtarget = mean_riemann(covs[ytest == 1])
Mnontarget = mean_riemann(covs[ytest == 0])
stats[condition]['distance'] = distance_riemann(Mtarget, Mnontarget)
stats[condition]['dispersion_target'] = np.sum(
[distance_riemann(covi, Mtarget)**2
for covi in covs[ytest == 1]]) / len(covs[ytest == 1])
stats[condition]['dispersion_nontarget'] = np.sum([
distance_riemann(covi, Mnontarget)**2 for covi in covs[ytest == 0]
]) / len(covs[ytest == 0])
print('subject', subject)
print(stats)
results[subject] = stats
picks=picks,
baseline=None,
preload=True,
verbose=False)
X = epochs.get_data()
y = epochs.events[:, -1]
###############################################################################
# Embedding the Xdawn covariance matrices with Laplacian Eigenmaps
nfilter = 4
xdwn = XdawnCovariances(estimator='scm', nfilter=nfilter)
split = train_test_split(X, y, train_size=0.25, random_state=42)
Xtrain, Xtest, ytrain, ytest = split
covs = xdwn.fit(Xtrain, ytrain).transform(Xtest)
lapl = Embedding(metric='riemann', n_components=2)
embd = lapl.fit_transform(covs)
###############################################################################
# Plot the three first components of the embedded points
fig, ax = plt.subplots(figsize=(7, 8), facecolor='white')
for cond, label in event_id.items():
idx = (ytest == label)
ax.scatter(embd[idx, 0], embd[idx, 1], s=36, label=cond)
ax.set_xlabel(r'$\varphi_1$', fontsize=16)
ax.set_ylabel(r'$\varphi_2$', fontsize=16)
