028274e8f52c54b10d632a61ee19a6e64f7a90e6,examples/MotorImagery/two_class_motor_imagery.py,,,#,24

Before Change

from moabb.datasets.physionet_mi import PhysionetMI
from moabb.datasets.openvibe_mi import OpenvibeMI

datasets = [AlexMI(), OpenvibeMI(), BNCI2015004(motor_imagery=True),
            PhysionetMI(), GigaDbMI(), BBCIEEGfNIRS()]

pipelines = OrderedDict()
pipelines["MDM"] = make_pipeline(Covariances("oas"), MDM())
pipelines["TS"] = make_pipeline(Covariances("oas"), TSclassifier())
pipelines["CSP+LDA"] = make_pipeline(Covariances("oas"), CSP(8), LDA())
// pipelines["CSP+SVM"] = make_pipeline(Covariances("oas"), CSP(8), SVC())

context = MotorImageryTwoClasses(datasets=datasets, pipelines=pipelines)

results = context.evaluate(verbose=True)

for p in results.keys():
    results[p].to_csv("../../results/MotorImagery/TwoClass/%s.csv2" % p)

results = pd.concat(results.values())
print(results.groupby("Pipeline").mean())

res = results.pivot(values="Score", columns="Pipeline")
sns.lmplot(data=res, x="CSP+LDA", y="TS", fit_reg=False)
plt.xlim(0.4, 1)
plt.ylim(0.4, 1)
plt.plot([0.4, 1], [0.4, 1], ls="--", c="k")

After Change

context = LeftRightImagery(pipelines, WithinSessionEvaluation(), datasets)

context.process()

for v in context.results.data.values():
    print(v)

Instances