Pseudo-Online Motor Imagery with Sliding Window

This example shows how to perform a pseudo-online motor imagery evaluation using sliding window overlap. The overlap parameter in the paradigm generates overlapping epochs from the original trials, simulating an online BCI scenario.

We use the BNCI2014-001 dataset with two Riemannian pipelines (MDM and FgMDM) and a within-session evaluation.

Number of trials per class: {np.str_('feet'): np.int64(400), np.str_('left_hand'): np.int64(389), np.str_('right_hand'): np.int64(387), np.str_('tongue'): np.int64(384)}
/home/runner/work/moabb/moabb/moabb/analysis/results.py:95: RuntimeWarning: Setting non-standard config type: "MOABB_RESULTS"
  set_config("MOABB_RESULTS", osp.join(osp.expanduser("~"), "mne_data"))

BNCI2014-001-WithinSession:   0%|          | 0/1 [00:00<?, ?it/s]
BNCI2014-001-WithinSession: 100%|██████████| 1/1 [00:39<00:00, 39.91s/it]
BNCI2014-001-WithinSession: 100%|██████████| 1/1 [00:39<00:00, 39.91s/it]
  pipeline     score       std
0    FgMDM  0.772436  0.031729
1      MDM  0.681410  0.004533

import numpy as np
from pyriemann.classification import MDM, FgMDM
from pyriemann.estimation import Covariances
from sklearn.pipeline import Pipeline

from moabb.datasets import BNCI2014_001
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import MotorImagery


sub = 1

# Initialize parameter for the Band Pass filter
fmin = 8
fmax = 30
tmax = 3

# Load dataset and configure overlap in the paradigm pipeline
dataset = BNCI2014_001()

events = list(dataset.event_id.keys())

paradigm = MotorImagery(
    events=events, n_classes=len(events), fmin=fmin, fmax=fmax, tmax=tmax, overlap=50
)

X, y, meta = paradigm.get_data(dataset=dataset, subjects=[sub])
unique, counts = np.unique(y, return_counts=True)
print("Number of trials per class:", dict(zip(unique, counts)))


pipelines = {}
pipelines["MDM"] = Pipeline(
    steps=[
        ("Covariances", Covariances("cov")),
        ("MDM", MDM(metric=dict(mean="riemann", distance="riemann"))),
    ]
)

pipelines["FgMDM"] = Pipeline(
    steps=[("Covariances", Covariances("cov")), ("FgMDM", FgMDM())]
)

dataset.subject_list = dataset.subject_list[int(sub) - 1 : int(sub)]
# Select an evaluation Within Session
evaluation_online = WithinSessionEvaluation(
    paradigm=paradigm, datasets=dataset, overwrite=True, random_state=42, n_jobs=1
)

# Print the results
results_ALL = evaluation_online.process(pipelines)
results_pipeline = results_ALL.groupby(["pipeline"], as_index=False)["score"].mean()
results_pipeline_std = results_ALL.groupby(["pipeline"], as_index=False)["score"].std()
results_pipeline["std"] = results_pipeline_std["score"]
print(results_pipeline)

Estimated memory usage: 1148 MB