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.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())

Zhou2016-WithinSession:   0%|          | 0/2 [00:00<?, ?it/s]
Zhou2016-WithinSession:  50%|█████     | 1/2 [00:03<00:03,  3.59s/it]
Zhou2016-WithinSession: 100%|██████████| 2/2 [00:06<00:00,  3.41s/it]
Zhou2016-WithinSession: 100%|██████████| 2/2 [00:06<00:00,  3.43s/it]

BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00<?, ?it/s]
BNCI2014-001-WithinSession:  50%|█████     | 1/2 [00:05<00:05,  5.18s/it]
BNCI2014-001-WithinSession: 100%|██████████| 2/2 [00:09<00:00,  4.81s/it]
BNCI2014-001-WithinSession: 100%|██████████| 2/2 [00:09<00:00,  4.86s/it]
      score      time  ...  pipeline                  codecarbon_task_name
0  0.847601  0.047893  ...     ts+lr  2dd18d9e-58ce-4839-8778-bda96f315583
1  0.922000  0.043081  ...     ts+lr  651199d9-3ec5-41fc-bd41-b7743ba1d9ed
2  0.952000  0.045720  ...     ts+lr  cf73a530-c110-43c5-99b4-a10c62713349
3  0.916000  0.045387  ...     ts+lr  49a2d4e5-4588-41d9-a720-d115b6f3bdaf
4  0.846914  0.041111  ...     ts+lr  f9d1e5f7-2929-45e5-9c2e-a6ee71f866c9

[5 rows x 11 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())

Zhou2016-WithinSession:   0%|          | 0/2 [00:00<?, ?it/s]
Zhou2016-WithinSession:  50%|█████     | 1/2 [00:03<00:03,  3.41s/it]
Zhou2016-WithinSession: 100%|██████████| 2/2 [00:07<00:00,  3.61s/it]
Zhou2016-WithinSession: 100%|██████████| 2/2 [00:07<00:00,  3.58s/it]

BNCI2014-001-WithinSession:   0%|          | 0/2 [00:00<?, ?it/s]
BNCI2014-001-WithinSession:  50%|█████     | 1/2 [00:05<00:05,  5.30s/it]
BNCI2014-001-WithinSession: 100%|██████████| 2/2 [00:10<00:00,  4.96s/it]
BNCI2014-001-WithinSession: 100%|██████████| 2/2 [00:10<00:00,  5.01s/it]
      score      time  ...  pipeline                  codecarbon_task_name
0  0.839141  0.051255  ...     ts+lr  fc7ba9ac-3007-4812-b0aa-e5321c7f5402
1  0.912000  0.041649  ...     ts+lr  6eb79699-8ecc-4e6b-9720-a6c37daa6240
2  0.950000  0.052880  ...     ts+lr  a3752541-cbe6-4a23-907e-2a714f32517a
3  0.930000  0.056838  ...     ts+lr  2b7f8dbc-1357-4f29-b8d9-10c79aa9ef1b
4  0.827160  0.051731  ...     ts+lr  ac2cc52f-a866-4c5c-ae11-e55494798f05

[5 rows x 11 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.

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