Select Electrodes and Resampling

Within paradigm, it is possible to restrict analysis only to a subset of electrodes and to resample to a specific sampling rate. There is also a utility function to select common electrodes shared between datasets. This tutorial demonstrates how to use this functionality.

# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
from mne.decoding import CSP
from pyriemann.estimation import Covariances
from pyriemann.tangentspace import TangentSpace
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.linear_model import LogisticRegression as LR
from sklearn.pipeline import make_pipeline

import moabb.analysis.plotting as moabb_plt
from moabb.analysis.chance_level import chance_by_chance
from moabb.datasets import BNCI2014_001, Zhou2016
from moabb.datasets.utils import find_intersecting_channels
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import LeftRightImagery

Datasets

Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each

subj = [1, 2]
datasets = [Zhou2016(), BNCI2014_001()]
for d in datasets:
    d.subject_list = subj

Paradigm

Restrict further analysis to specified channels, here C3, C4, and Cz. Also, use a specific resampling. In this example, all datasets are set to 200 Hz.

paradigm = LeftRightImagery(channels=["C3", "C4", "Cz"], resample=200.0)

Evaluation

The evaluation is conducted on with CSP+LDA, only on the 3 electrodes, with a sampling rate of 200 Hz.

evaluation = WithinSessionEvaluation(paradigm=paradigm, datasets=datasets)
csp_lda = make_pipeline(CSP(n_components=2), LDA())
ts_lr = make_pipeline(
    Covariances(estimator="oas"), TangentSpace(metric="riemann"), LR(C=1.0)
)
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())

      score      time  samples  ...   dataset  pipeline  codecarbon_task_name
0  0.938000  0.019291    100.0  ...  Zhou2016   csp+lda
1  0.853788  0.018971    119.0  ...  Zhou2016   csp+lda
2  0.892000  0.017121    100.0  ...  Zhou2016   csp+lda
3  0.918000  0.015647    100.0  ...  Zhou2016   csp+lda
4  0.920000  0.015703    100.0  ...  Zhou2016   csp+lda

[5 rows x 13 columns]

Electrode Selection

It is possible to select the electrodes that are shared by all datasets using the find_intersecting_channels function. Datasets that have 0 overlap with others are discarded. It returns the set of common channels, as well as the list of datasets with valid channels.

electrodes, datasets = find_intersecting_channels(datasets)
evaluation = WithinSessionEvaluation(
    paradigm=paradigm, datasets=datasets, overwrite=True, suffix="resample"
)
results = evaluation.process({"csp+lda": csp_lda, "ts+lr": ts_lr})
print(results.head())

      score      time  samples  ...   dataset  pipeline  codecarbon_task_name
0  0.962000  0.014121    100.0  ...  Zhou2016   csp+lda
1  0.896000  0.014064    100.0  ...  Zhou2016   csp+lda
2  0.860732  0.015478    119.0  ...  Zhou2016   csp+lda
3  0.934000  0.013948    100.0  ...  Zhou2016   csp+lda
4  0.834568  0.013213     90.0  ...  Zhou2016   csp+lda

[5 rows x 13 columns]

Plot Results

Compare the obtained results with the two pipelines, CSP+LDA and logistic regression computed in the tangent space of the covariance matrices.

chance_levels = chance_by_chance(results, alpha=[0.05, 0.01])

fig = moabb_plt.paired_plot(results, "csp+lda", "ts+lr", chance_level=chance_levels)
plt.show()