Ensemble learning on functional connectivity¶

This example shows how to compute SPD matrices from functional connectivity estimators and how to combine classification with ensemble learning 1.

```# Authors: Sylvain Chevallier <sylvain.chevallier@universite-paris-saclay.fr>,
#          Marie-Constance Corsi <marie.constance.corsi@gmail.com>
#

import matplotlib.pyplot as plt

from mne import Epochs, pick_types, events_from_annotations
from mne.io import concatenate_raws
from mne.datasets import eegbci

import numpy as np
import pandas as pd
import seaborn as sns

from pyriemann.classification import FgMDM
from pyriemann.estimation import Covariances, Coherences
from pyriemann.spatialfilters import CSP
from pyriemann.tangentspace import TangentSpace

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.ensemble import StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC

from helpers.coherence_helpers import (
NearestSPD,
get_results,
)
```

Define connectivity transformer¶

This estimator computes the functional connectivity from input signal using pyriemann.estimation.Coherences

```class Connectivities(TransformerMixin, BaseEstimator):
"""Getting connectivity features from epoch"""

def __init__(self, method="ordinary", fmin=8, fmax=35, fs=None):
self.method = method
self.fmin = fmin
self.fmax = fmax
self.fs = fs

def fit(self, X, y=None):
self._coh = Coherences(
coh=self.method,
fmin=self.fmin,
fmax=self.fmax,
fs=self.fs,
)
return self

def transform(self, X):
X_coh = self._coh.fit_transform(X)
X_con = np.mean(X_coh, axis=-1, keepdims=False)
return X_con
```

```# avoid classification of evoked responses by using epochs that start 1s after
# cue onset.
tmin, tmax = 1.0, 2.0
event_id = dict(hands=2, feet=3)
subject = 7
runs = [4, 8]  # motor imagery: left vs right hand

raw_files = [
]
raw = concatenate_raws(raw_files)

picks = pick_types(
raw.info, meg=False, eeg=True, stim=False, eog=False, exclude="bads"
)
# subsample elecs
picks = picks[::2]

# Apply band-pass filter
raw.filter(7.0, 35.0, method="iir", picks=picks)

events, _ = events_from_annotations(raw, event_id=dict(T1=2, T2=3))

# Read epochs (train will be done only between 1 and 2s)
epochs = Epochs(
raw,
events,
event_id,
tmin,
tmax,
proj=True,
picks=picks,
baseline=None,
verbose=False,
)
labels = epochs.events[:, -1] - 2
fs = epochs.info["sfreq"]
X = 1e6 * epochs.get_data()
```
```Extracting EDF parameters from /home/docs/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S007/S007R04.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Extracting EDF parameters from /home/docs/mne_data/MNE-eegbci-data/files/eegmmidb/1.0.0/S007/S007R08.edf...
EDF file detected
Setting channel info structure...
Creating raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Filtering a subset of channels. The highpass and lowpass values in the measurement info will not be updated.
Filtering raw data in 2 contiguous segments
Setting up band-pass filter from 7 - 35 Hz

IIR filter parameters
---------------------
Butterworth bandpass zero-phase (two-pass forward and reverse) non-causal filter:
- Filter order 16 (effective, after forward-backward)
- Cutoffs at 7.00, 35.00 Hz: -6.02, -6.02 dB

Used Annotations descriptions: ['T1', 'T2']
```

Defining pipelines¶

Compare CSP+SVM, FgMDM on covariance, tangent space logistic regression with covariance, lag coherence, and instantaneous coherence, along with ensemble method

```ppl_baseline, ppl_fc, ppl_ens = {}, {}, {}
```

Baseline algorithms are CSP with optimal SVM and FgMDM based on covariances

```param_svm = {"kernel": ("linear", "rbf"), "C": [0.1, 1, 10]}
step_csp = [
("cov", Covariances(estimator="lwf")),
("csp", CSP(nfilter=6)),
("optsvm", GridSearchCV(SVC(), param_svm, cv=3)),
]
ppl_baseline["CSP+optSVM"] = Pipeline(steps=step_csp)

step_mdm = [
("cov", Covariances(estimator="lwf")),
("fgmdm", FgMDM(metric="riemann", tsupdate=False)),
]
ppl_baseline["FgMDM"] = Pipeline(steps=step_mdm)
```

Functional connectivity pipelines use logistic regression in tangent space. They will be estimated from covariance, lagged coherence and instantaneous coherence.

```spectral_met = ["cov", "lagged", "instantaneous"]
fmin, fmax = 8, 35
param_lr = {
"penalty": "elasticnet",
"l1_ratio": 0.15,
"intercept_scaling": 1000.0,
"solver": "saga",
}
param_ft = {"fmin": fmin, "fmax": fmax, "fs": fs}
step_fc = [
("spd", NearestSPD()),
("tg", TangentSpace(metric="riemann")),
("LogistReg", LogisticRegression(**param_lr)),
]
for sm in spectral_met:
pname = sm + "+elasticnet"
if sm == "cov":
ppl_fc[pname] = Pipeline(
steps=[("cov", Covariances(estimator="lwf"))] + step_fc
)
else:
ft = Connectivities(**param_ft, method=sm)
ppl_fc[pname] = Pipeline(steps=[("ft", ft)] + step_fc)
```

The ensemble classifier stacks a logistic regression on top of the three functional connectivity pipelines to make a global prediction

```fc_estim = [(n, ppl_fc[n]) for n in ppl_fc]
cvkf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

lr = LogisticRegression(**param_lr)
ppl_ens["ensemble"] = StackingClassifier(
estimators=fc_estim,
cv=cvkf,
n_jobs=1,
final_estimator=lr,
stack_method="predict_proba",
)
```

Evaluation¶

```dataset_res = list()
all_ppl = {**ppl_baseline, **ppl_ens}

# Compute results
results = get_results(X, labels, all_ppl)
results = pd.DataFrame(results)
```
```/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/checkouts/v0.4/pyriemann/utils/mean.py:470: UserWarning: Convergence not reached
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
warnings.warn('Convergence not reached')
/home/docs/checkouts/readthedocs.org/user_builds/pyriemann/envs/v0.4/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
ConvergenceWarning,
```

Plot¶

```list_fc_ens = ["ensemble", "CSP+optSVM", "FgMDM"] + \
[sm + "+elasticnet" for sm in spectral_met]

g = sns.catplot(
data=results,
x="pipeline",
y="score",
kind="bar",
order=list_fc_ens,
height=7,
aspect=2,
)
plt.show()
```

References¶

1

Functional connectivity ensemble method to enhance BCI performance (FUCONE) Corsi, M.-C., Chevallier, S., De Vico Fallani, F. & Yger, F. IEEE TBME, 2022

Total running time of the script: ( 0 minutes 42.124 seconds)

Gallery generated by Sphinx-Gallery