mne_rt.combiners.ZScoredNormCombiner

class mne_rt.combiners.ZScoredNormCombiner(features: list[str], warmup: int = 30)

Bases: FeatureCombiner

Euclidean norm after online z-score normalisation of each feature.

Each feature stream is independently normalised using the mean and standard deviation estimated from the first warmup windows:

zᵢ = (xᵢ − μᵢ) / σᵢ
mixed = ‖z‖ / √n  =  sqrt(Σ zᵢ²) / sqrt(n)

Dividing by √n keeps the output near 1 when all features hover at their baseline mean, and it grows when any feature deviates — making it a natural “how different from baseline are we?” score.

Statistics are frozen after warmup (no drift tracking). To re-fit (e.g. between blocks), call reset().

Parameters:

featureslist of str

Modality names to include.

warmupint, default 30

Number of windows collected before statistics are fixed and the combiner starts producing non-zero output. Returns 0.0 during the warmup phase.

Notes

If a feature’s standard deviation is effectively zero (constant signal), it is floored at 1e-9 to prevent division-by-zero.

Examples

from mne_rt.combiners import ZScoredNormCombiner

combiner = ZScoredNormCombiner(
    features=["sensor_power", "laterality", "connectivity_ratio"],
    warmup=30,
)
for window_vals in session_data:         # first 30 calls return 0.0
    mixed = combiner.combine(window_vals)

__init__(features: list[str], warmup: int = 30) → None

Methods

__init__(features[, warmup])
combine(values)	Return the z-scored Euclidean norm, or 0.0 during warmup.
reset()	Clear collected statistics and restart the warmup phase.

reset() → None

Clear collected statistics and restart the warmup phase.

Useful when called between NF blocks so the combiner re-fits its baseline to the new block’s distribution.

combine(values: dict[str, float]) → float

Return the z-scored Euclidean norm, or 0.0 during warmup.