Paradigms & profiles

from nimbus_bci import Personalizer, wrap

enc = wrap(model.encode, model_id="partner", embedding_dim=64)
p = Personalizer.for_deployment(enc, ["left", "right"])
p.fit(X_cal, y_cal)

profile = p.export_profile()          # head + transform + thresholds + cal Z
warm = Personalizer.from_profile(profile, encoder=enc)
warm.partial_fit(X_day2[:40], y_day2[:40])  # optional top-up

from nimbus_bci import Personalizer

# OAS / tangent features from epochs — needs [riemann]
p = Personalizer.for_riemann(["left", "right"])  # tracking_alpha=0.05 default
p.fit(X_cal, y_cal)  # epochs (n, ch, t) or already-mapped features per API
p.adapt(epoch)       # label-free reference update when tracking is on

from nimbus_bci.middleware.mi_session import fit_mi_session, MISessionBundle

bundle = fit_mi_session(X_cal, y_cal, classes=["left", "right"], sfreq=250.0)
# bundle.personalizer is fitted; bundle.path is deployment_z (CSP) or features_riemann

bundle.save("subject_mi")
# Day 2 — pickle sidecars require trust + digest check:
loaded = MISessionBundle.load("subject_mi", trusted=True)

from nimbus_bci import Personalizer
from nimbus_bci.middleware.p300_session import fit_p300_session, P300SessionBundle

# Factory — tracking off by default; enable with tracking_alpha=0.05
p = Personalizer.for_p300(["NonTarget", "Target"], tracking_alpha=0.05)

# Or one-call session fit (xDAWN→tangent → LDA)
bundle = fit_p300_session(
    X_cal, y_cal,
    classes=["NonTarget", "Target"],
    tracking_alpha=0.05,  # recommend 0.05 when enabling; 0.02 not stability-cleared
)
bundle.save("subject_p300")
loaded = P300SessionBundle.load("subject_p300", trusted=True)

p = Personalizer.for_research(enc, ["left", "right"], transform="whiten")
p.fit(X_cal, y_cal)
# coral / affine need a stream slice after fit:
# p.fit_coral(X_stream) / p.fit_affine(X_stream, y_stream)

p = Personalizer.for_deployment(enc, ["left", "right"])
p.fit_calibrated(X_cal, y_cal, X_val, y_val, target_accept=0.7)