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Anti-alias FIR filter followed by integer-factor decimation — explicit pre-roll for cross-band pipelines
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Basado en la clasificación ocupacional SOC
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| name | anti_alias_resample |
| description | Anti-alias FIR filter followed by integer-factor decimation — explicit pre-roll for cross-band pipelines |
| layer | L3 |
| group | filter |
| metadata | {"tags":["operator","filter","resample","anti_alias","decimate","downsample"],"modalities":["eeg","meg","seeg","ecog","fnirs","lfp","spike"],"step_string":"anti_alias_resample","analysis_goal_allowed":["classification","source_localization","feature_extraction","clinical_screening","exploratory","generic","connectivity","phase_amplitude_coupling","online_inference"],"analysis_goal_forbidden":[]} |
Two-step downsampling: apply a low-pass FIR anti-alias filter at the
target Nyquist, then decimate by an integer factor. Unlike the generic
resample operator (which fuses both steps in a single library call),
this operator exposes the anti-alias cut-off explicitly — important when
the downstream analysis crosses bands (e.g., AP / LF dual stream on
Neuropixels) and you want to control the transition band manually.
Input / Output: (n_channels, n_times) → (n_channels, n_times // factor),
frequency updated to frequency / factor.
f_c = target_rate · 0.45 (with default 10%
guard below Nyquist) using scipy.signal.firwin + Hamming window.data[:, ::factor] selects every Nth sample. The phase
is preserved (FIR is linear-phase).Choice vs. scipy.signal.resample_poly:
| Path | When |
|---|---|
firwin + decimate (this op) | Integer factor; cross-band pipelines where you need the explicit anti-alias breakpoint. |
resample_poly (generic resample) | Rational factor (e.g., 2048 → 1000); single library call; cleaner code. |
anti_alias_resample:{target_rate} — comma-separated:
| Parameter | Type | Default | Description |
|---|---|---|---|
target_rate | float | required | Output sampling rate (Hz). |
numtaps (kw) | int | auto | FIR length; auto = max(101, round(50 * source / target)). |
guard_fraction (kw) | float | 0.10 | Cut-off as (target/2) · (1 − guard_fraction). |
Examples:
anti_alias_resample:1000 — Neuropixels AP 30 kHz → 1 kHz LFP-band-grade.anti_alias_resample:256 — EEG 1024 Hz → 256 Hz (ML model input rate).| Modality | Source rate | Target rate | Notes |
|---|---|---|---|
| EEG | 1024 / 2048 | 256 | Standard ML target. |
| MEG | 2 kHz | 1 kHz | Halve for offline analysis. |
| sEEG | 2 kHz | 500 | When only LFP-band needed. |
| Spike AP | 30 kHz | 30 kHz (no-op) | Don't downsample spike-band; threshold needs full rate. |
| LFP | 2.5 kHz | 1 kHz | Common downsample. |
EasyBCIOperatorError(recoverable=False) when
target_rate < 20 kHz and the input is flagged spike-AP modality.Use when: target rate is an integer divisor; you need an explicit anti-alias cut-off (cross-band).
Don't use when: factor is non-integer (use resample); target is
spike AP band; you've already low-passed at the target Nyquist (no-op).
resample.bandpass whose high ≤ target Nyquist.notch — line-noise harmonics must be removed before
the anti-alias cuts above target Nyquist (otherwise harmonics fold).| Failure | Symptom | Detection |
|---|---|---|
| Non-integer factor | factor = source / target is non-integer | Pre-check; raise recoverable with fallback_step="resample:{target}". |
| Spike-band downsample | sfreq dropping below 20 kHz on spike modality | Hard error; recoverable=False. |
| Aliased output | PSD shows energy folding above target Nyquist | Compare PSD pre/post; flag if folded energy > 10% of in-band. |
from __future__ import annotations
import numpy as np
from scipy.signal import firwin, filtfilt
def anti_alias_resample(
data: np.ndarray,
sfreq: float,
target_rate: float,
*,
numtaps: int | None = None,
guard_fraction: float = 0.10,
) -> tuple[np.ndarray, float]:
"""Anti-alias then decimate.
Returns
-------
out : ndarray (n_channels, n_times // factor), float32
new_sfreq : float
"""
factor = sfreq / target_rate
if abs(factor - round(factor)) > 1e-6:
raise ValueError(
f"anti_alias_resample: factor {factor:.3f} not integer; "
f"use polyphase resample"
)
factor = int(round(factor))
if numtaps is None:
numtaps = max(101, int(round(50 * factor)))
numtaps |= 1 # odd
cutoff = (target_rate / 2.0) * (1.0 - guard_fraction)
h = firwin(numtaps, cutoff, fs=sfreq, window="hamming")
filtered = filtfilt(h, [1.0], data, axis=-1, padtype="even")
out = filtered[..., ::factor].astype(data.dtype, copy=False)
return out, sfreq / factor
from typing import Any, Dict
import time
import numpy as np
from easybci_lib.tools.neural_processing.operator_errors import EasyBCIOperatorError
from easybci_lib.tools.neural_processing.preprocess.step_cache import record_step_elapsed
def operator_anti_alias_resample(
data_dict: Dict[str, Any],
*,
target_rate: float,
numtaps: int | None = None,
guard_fraction: float = 0.10,
) -> Dict[str, Any]:
"""Anti-alias FIR + integer decimate.
Parameters
----------
data_dict : dict
OperatorIO.
target_rate : float
Output sampling rate (Hz).
numtaps : int or None
FIR length (default auto).
guard_fraction : float
Cut-off guard below Nyquist (default 0.10).
Returns
-------
dict
OperatorIO with `data` downsampled, `frequency` updated.
Raises
------
EasyBCIOperatorError
recoverable=True for non-integer factor; recoverable=False if
downsampling spike-AP below 20 kHz.
Modality coverage
-----------------
EEG / MEG / sEEG / ECoG / fNIRS / LFP: yes.
Spike AP-band (sfreq >= 20 kHz to < 20 kHz): forbidden.
References
----------
Oppenheim & Schafer (1989) DSP textbook; scipy.signal.firwin docs.
"""
src = float(data_dict["frequency"])
modality = (data_dict.get("meta") or {}).get("modality", "").lower()
if modality == "spike" and target_rate < 20_000:
raise EasyBCIOperatorError(
operator="anti_alias_resample",
reason=f"refusing to downsample spike-AP to {target_rate} Hz (< 20 kHz)",
recoverable=False,
)
factor = src / target_rate
if abs(factor - round(factor)) > 1e-6:
raise EasyBCIOperatorError(
operator="anti_alias_resample",
reason=f"non-integer factor {factor:.3f}; use generic resample",
recoverable=True,
fallback_step=f"resample:{int(target_rate)}",
)
t0 = time.monotonic()
from scipy.signal import firwin, filtfilt
factor_int = int(round(factor))
if numtaps is None:
numtaps = max(101, int(round(50 * factor_int))) | 1
cutoff = (target_rate / 2.0) * (1.0 - guard_fraction)
h = firwin(numtaps, cutoff, fs=src, window="hamming")
filtered = filtfilt(h, [1.0], data_dict["data"], axis=-1, padtype="even")
new_data = filtered[..., ::factor_int].astype(data_dict["data"].dtype, copy=False)
elapsed = time.monotonic() - t0
out = dict(data_dict)
out["data"] = new_data
out["frequency"] = src / factor_int
out["duration"] = new_data.shape[-1] / out["frequency"]
out["elapsed_s"] = elapsed
out["meta"] = {**out.get("meta", {}), "anti_alias_resample": {
"target_rate": target_rate, "numtaps": numtaps, "factor": factor_int,
}}
record_step_elapsed(
"anti_alias_resample", elapsed,
(data_dict.get("meta") or {}).get("step_cache_key"),
)
return out