nb-2-8-pipeline · Pipeline order and reproducibility (L2.8)¶
Lesson L2.8 · Level 2 · Status draft — for expert review; uncertain points carry TODO(confirm).
Everything in Level 2 becomes one function here: run_subject(config, subject) executing the canonical order
load → montage → bad-channel detection → filter → interpolate → re-reference → ICA → epoch → reject
driven entirely by a configuration file, logging every step, and emitting a per-subject QC HTML report.
- Which implementation is running — the repository's
pipelines/eegpipepackage if it is there, and the same documented interface innotebooks/_shared/helpers_l2.pyif it is not. - The configuration: one YAML file, validated, with a hash.
- One subject end to end, with the run log: what each step did, how long it took, and the resolved parameter values rather than the requested ones.
- The QC report, rendered inline.
- Two runs differ only by their configuration — one parameter changed, and the consequences read off the two logs.
- A failure is a row, not a stopped cohort.
Where the code lives. The Phase 2 build contract reserves pipelines/eegpipe for this package — config.py, steps/ (one module per step), run.py with run_subject(config, subject) -> RunResult, qc.py (the HTML report), cli.py and tests/. The package is imported without being installed, by walking up from the working directory to the repository's pipelines/ folder — the same trick that finds _shared/, and no absolute path anywhere.
Data. ds-erpcore P3 (CC BY-SA 4.0 per data/directory.yaml, contested at source; open; per-subject downloadable): sub-001 for the worked run (~56 MB on an empty cache; already-cached subjects are re-used). TODO(confirm): the author mirrors the ERP CORE entry into the catalog registry and signs off the dataset page (§10.11 item 8).
In [1]:
# Setup: dependencies, the shared helpers, non-interactive plotting.
import importlib.util
import subprocess
import sys
import warnings
from pathlib import Path
# 1. Dependencies are pinned in notebooks/requirements.txt. Nothing is installed
# when the pinned stack is already present (local runs, CI); a fresh Colab or
# Binder kernel installs it once. On Colab, run from a clone of the repository
# so that notebooks/_shared/ is available (repository URL: TODO(confirm), spec
# section 13 item 3).
_needed = ('mne', 'scipy', 'matplotlib', 'pooch', 'pyprep', 'autoreject', 'yaml')
_missing = [p for p in _needed if importlib.util.find_spec(p) is None]
if _missing:
_req = next((d / "requirements.txt" for d in (Path.cwd(), *Path.cwd().parents)
if (d / "requirements.txt").exists()), None)
_cmd = [sys.executable, "-m", "pip", "install", "-q"]
_cmd += ["-r", str(_req)] if _req else ["mne==1.10.2", "pooch>=1.8"]
if "pyprep" in _missing:
_cmd += ["pyprep>=0.9"]
if "autoreject" in _missing:
_cmd += ["autoreject>=0.5"]
if "yaml" in _missing:
_cmd += ["pyyaml>=6"]
subprocess.check_call(_cmd)
# 2. Shared helpers (notebooks/_shared/helpers.py and helpers_l2.py), located
# relative to the working directory -- notebooks/<level>/ or notebooks/ --
# never through an absolute path.
_shared = next((d / "_shared" for d in (Path.cwd(), *Path.cwd().parents)
if (d / "_shared" / "helpers_l2.py").exists()), None)
if _shared is None:
raise FileNotFoundError("start the kernel in notebooks/L2/ (or notebooks/) so that _shared/helpers_l2.py is found")
sys.path.insert(0, str(_shared))
import helpers
import helpers_l2 as l2
# 3. Plotting: Jupyter's default inline backend renders static PNGs through Agg
# (no windows, nothing blocks); outside Jupyter the helpers select Agg. Every
# MNE figure is requested with show=False, and plt.show() renders each cell's
# figures in place.
import matplotlib.pyplot as plt
import numpy as np
import mne
# Warnings are worth reading, so they are not silenced -- but their default format prints the
# absolute path of the file that raised them, which is nobody else's business and would put this
# machine's directory layout into the saved outputs. Only the class and the message are shown.
warnings.formatwarning = lambda message, category, *a, **k: f"{category.__name__}: {message}\n"
mne.viz.set_browser_backend("matplotlib", verbose=False)
mne.set_log_level("WARNING")
plt.rcParams["figure.dpi"] = 72
print(f"MNE {mne.__version__}; helpers imported from notebooks/_shared")
print("ERP CORE downloads (~56 MB per subject) are cached under data/downloads/erpcore/ in a repository "
"clone, otherwise under MNE's data directory; nothing is re-fetched.")
1. Which implementation is running¶
In [2]:
import tempfile
import time
from IPython.display import HTML, display
_pipelines = next((d / "pipelines" for d in (Path.cwd(), *Path.cwd().parents)
if (d / "pipelines" / "eegpipe" / "__init__.py").exists()), None)
EEGPIPE = None
if _pipelines is not None:
sys.path.insert(0, str(_pipelines))
try:
import eegpipe as EEGPIPE
except Exception as exc:
print(f"pipelines/eegpipe found but not importable: {type(exc).__name__}: {exc}")
if EEGPIPE is not None:
print(f"implementation: pipelines/eegpipe {EEGPIPE.__version__} (the package the Phase 2 contract reserves)")
print(f" canonical order: {' -> '.join(EEGPIPE.CANONICAL_ORDER)}")
print(f" step modules: {', '.join(EEGPIPE.STEPS)}")
run_one = EEGPIPE.run_subject
else:
print("implementation: helpers_l2.run_subject -- the same documented interface "
"(site/CONTRACTS.md, Phase 2 addendum), used because pipelines/eegpipe is not importable here")
run_one = l2.run_subject
print()
print("Documented interface (site/CONTRACTS.md, Phase 2 addendum):")
print(" config.py dataclass schema + YAML load/validate")
print(" steps/ load, montage, bad_channels, filter, interpolate, reference, ica, epoch, reject")
print(" run.py run_subject(config, subject) -> RunResult")
print(" qc.py HTML report: bad-channel table, ICA components removed with reasons, percent data")
print(" rejected per condition, filter settings, run log with versions and seed")
print(" cli.py python -m eegpipe run --config ...")
print(" tests/ pytest, including a fast synthetic test that downloads nothing")
In [3]:
# Both implementations are read through one small adapter, so every table below is written once.
def norm(res):
"""One shape for eegpipe.RunResult and for helpers_l2.run_subject's dict."""
if hasattr(res, "status"): # eegpipe.RunResult
bads = {ch: v.get("criteria", []) for ch, v in (res.bad_channels or {}).items()}
removed = [{"component": e["index"], "class": e["label"],
"probability": e.get("score", float("nan")), "evidence": e["reason"]}
for e in res.ica.get("excluded", [])]
per = (res.rejection or {}).get("per_condition", {})
table = [{"condition": c, "n_presented": v["n_before"], "n_rejected": v["n_rejected"],
"n_kept": v["n_kept"], "percent_rejected": v["percent_rejected"]} for c, v in per.items()]
table.append({"condition": "all", "n_presented": res.rejection.get("n_epochs_before", 0),
"n_rejected": res.rejection.get("n_epochs_before", 0) - res.rejection.get("n_epochs_after", 0),
"n_kept": res.rejection.get("n_epochs_after", 0),
"percent_rejected": res.rejection.get("percent_rejected", float("nan"))})
return {"subject": res.subject, "ok": res.status == "ok", "error": res.error,
"steps": [{"name": s["name"], "duration_s": s["duration_s"], "params": s.get("params", {}),
"notes": s.get("notes", "")} for s in res.steps],
"bads": bads, "interpolated": list(res.interpolated or []), "rank": res.rank,
"rank_why": res.ica.get("rank_why", ""), "filter": res.filter or {},
"ica": {"n_components": res.ica.get("n_components"), "method": res.ica.get("method"),
"seed": res.ica.get("seed"), "fitted_on": res.ica.get("fitted_on"),
"rank_after": res.ica.get("rank_after"), "removed": removed,
"kept_by_cap": res.ica.get("kept_by_max_remove", []),
"converged": res.ica.get("converged")},
"rejection": {"criterion": res.rejection.get("criterion", {}), "table": table,
"imbalance_pp": res.rejection.get("condition_imbalance_pp", float("nan"))},
"flags": list(res.flags or []), "outputs": dict(res.outputs or {}),
"config_hash": res.config_hash, "seed": res.seed, "versions": res.versions,
"duration_s": res.duration_s}
d = res # helpers_l2.run_subject
det = d.get("bad_channels", {}).get("detection", {})
dec = d.get("bad_channels", {}).get("decision", {})
return {"subject": d["subject"], "ok": d["ok"], "error": d["error"],
"steps": d["log"].steps, "bads": det.get("by_channel", {}),
"interpolated": dec.get("bads", []), "rank": d.get("rank", {}).get("rank"),
"rank_why": d.get("rank", {}).get("arithmetic", ""), "filter": d.get("filter_resolved", {}),
"ica": {"n_components": d.get("ica", {}).get("log", {}).get("n_components"),
"method": d.get("ica", {}).get("log", {}).get("method"),
"seed": d.get("ica", {}).get("log", {}).get("seed"),
"fitted_on": d.get("ica", {}).get("log", {}).get("fit_copy"),
"rank_after": d.get("ica", {}).get("rank_after"),
"removed": d.get("ica", {}).get("removed", []), "kept_by_cap": [], "converged": None},
"rejection": {"criterion": {"peak_to_peak_uv": d.get("rejection", {}).get("threshold_uv")},
"table": d.get("rejection", {}).get("table", []),
"imbalance_pp": float("nan")},
"flags": [], "outputs": {}, "config_hash": d.get("config_hash"), "seed": d["config"].get("seed"),
"versions": d.get("versions", {}), "duration_s": d.get("duration_s", float("nan"))}
2. The configuration¶
One file holds every parameter: the subject list and the exclusions with their reasons, the montage, the filter cutoffs and their justification, the detection thresholds, the reference, the ICA method and rank policy, the rejection criteria, the seed. Nothing is typed at a prompt; nothing is commented out to switch behaviour; no parameter appears twice.
The practical test the lesson gives is: can you reproduce last month's result by checking out the configuration file? The hash below is what a run log records so that question has an answer.
Two overrides are applied here, both of them arguments rather than edits: the output goes to a temporary directory, and ICLabel is switched on, because mne-icalabel is installed in this environment and the lesson wants the classifier's second opinion (L2.6).
In [4]:
_out = tempfile.TemporaryDirectory(prefix="nb-2-8-")
OUT = Path(_out.name)
if EEGPIPE is not None:
CONFIG_PATH = _pipelines / "configs" / "erpcore-p3.yaml"
OVERRIDES = {"output.dir": str(OUT), "steps.ica.iclabel.enabled": True,
"subjects.include": ["sub-001"]}
SHOWN_OVERRIDES = {**OVERRIDES, "output.dir": "<a temporary directory>"}
config = EEGPIPE.load_config(CONFIG_PATH, overrides=OVERRIDES)
text = CONFIG_PATH.read_text(encoding="utf-8")
print(f"configuration file: {CONFIG_PATH.relative_to(_pipelines.parent)} "
f"({len(text.splitlines())} lines, {len(text) / 1000:.1f} kB)")
print(f"overrides applied as arguments, not edits: {SHOWN_OVERRIDES}")
print(f"name: {config.name!r}; dataset {config.dataset.id} ({config.dataset.paradigm}); "
f"seed {config.seed}")
print()
print("".join(l for l in text.splitlines(keepends=True)
if not l.startswith("#"))[:2600].rstrip() + "\n ...")
else:
config = l2.PipelineConfig(subjects=("sub-001",), seed=l2.SEED)
print(config.to_yaml())
print()
print("The configuration's hash is recorded by every run (printed with the run log in section 3).")
3. One subject, end to end¶
The run log is not a debug convenience, it is the evidence. Each step records its name, its parameters as used (after defaults are resolved), its duration, and what it did.
In [5]:
t0 = time.time()
raw_result = run_one(config, "sub-001") if EEGPIPE is None else run_one(config, "sub-001", progress=False)
R = norm(raw_result)
print(f"\nreturned in {time.time() - t0:.0f} s; ok = {R['ok']}; configuration hash {R['config_hash']}, "
f"seed {R['seed']}")
print()
print("Run log:")
print(l2.fmt_table([{"step": s["name"], "duration (s)": s["duration_s"],
"notes": str(s.get("notes", ""))[:78]} for s in R["steps"]],
["step", "duration (s)", "notes"], floatfmt="{:.2f}"))
print(f" total {R['duration_s']:.1f} s")
if R["flags"]:
print(f"\nflags raised by the run (recorded, not fatal): {R['flags']}")
In [6]:
print(f"Bad channels ({len(R['bads'])} flagged, {len(R['interpolated'])} interpolated):")
print(l2.fmt_table([{"channel": ch, "criteria": crit,
"decision": "interpolated" if ch in R["interpolated"] else "kept"}
for ch, crit in R["bads"].items()]
or [{"channel": "(none flagged)", "criteria": "-", "decision": "-"}],
["channel", "criteria", "decision"]))
print()
print(f"Rank: {R['rank']} ({R['rank_why']})")
print()
print("Filter as resolved -- the values actually used, not the values requested:")
f = R["filter"]
flat = {k: v for k, v in f.items() if not isinstance(v, dict)}
print(l2.fmt_table([{"field": k, "value": str(v)[:70]} for k, v in flat.items()], ["field", "value"]))
for sub in ("analysis", "ica_copy"):
if isinstance(f.get(sub), dict):
print(f" {sub}: " + ", ".join(f"{k} {v}" for k, v in f[sub].items() if k != "why"))
if f[sub].get("why"):
print(f" why: {f[sub]['why']}")
for k, v in (f.get("justification") or {}).items():
print(f" justification, {k}: {' '.join(str(v).split())}")
In [7]:
ica = R["ica"]
print(f"ICA: {ica['method']}, {ica['n_components']} components, seed {ica['seed']}, fitted on "
f"{ica['fitted_on']}; converged: {ica['converged']}")
print(f" components removed: {len(ica['removed'])}; rank after cleaning {ica['rank_after']}")
print(l2.fmt_table(ica["removed"] or [{"component": "-", "class": "-", "probability": float("nan"),
"evidence": "nothing met the policy"}],
["component", "class", "probability", "evidence"], floatfmt="{:.3f}"))
if ica["kept_by_cap"]:
print(f" kept only because the cap was reached (logged, not silently dropped): {ica['kept_by_cap']}")
print()
print(f"Rejection criterion (one criterion for every condition, fixed before the data were looked at): "
f"{R['rejection']['criterion']}")
print(l2.fmt_table(R["rejection"]["table"],
["condition", "n_presented", "n_rejected", "n_kept", "percent_rejected"], floatfmt="{:.2f}"))
print(f" per-condition imbalance: {R['rejection']['imbalance_pp']:.2f} percentage points")
print()
print(f"Files written: " + ", ".join(f"{k} -> {Path(v).name}" for k, v in R["outputs"].items())
if R["outputs"] else "Files written: none (this implementation returns objects rather than files)")
4. The QC report¶
One HTML page per subject, self-contained, readable by someone who did not write the pipeline: the numbers that would make you distrust the subject at the top, then the bad-channel table, the removed components with their evidence, the per-condition rejection, the filter settings with a sentence each, and the run log with versions and the seed.
In [8]:
qc_path = R["outputs"].get("qc")
if qc_path and Path(qc_path).exists():
html = Path(qc_path).read_text(encoding="utf-8")
print(f"{Path(qc_path).name}: {len(html) / 1000:.0f} kB of self-contained HTML "
"(every figure embedded as a base64 PNG)")
else:
html = l2.qc_report_html(raw_result, title="QC report -- sub-001 (ds-erpcore P3)")
print(f"rendered by helpers_l2.qc_report_html: {len(html) / 1000:.0f} kB")
display(HTML(html))
QC report — sub-001 ok
ds-erpcore · configuration erpcore-p3
(hash 17f71c09f4cf) · seed 20260917
· 2026-09-18T14:22:11+00:00 · 13.8 s
Read this first — what would make you distrust this subject
- ICA did not converge (500 iterations)
0bad channels
0interpolated
24rank
5ICA removed
191epochs kept
4.5% rejected
Bad channels
30 EEG channels, 93 windows of 5.0 s. Criteria and thresholds are in the configuration below; detection ran before filtering and before re-referencing.
(nothing to show)
Rank after interpolation and re-referencing: 24 — 0 interpolated channel(s) (none) and the reference each cost one. This is the rank the ICA step was given.
Filter settings, as resolved
| what | high-pass (Hz) | low-pass (Hz) | method | phase | length (samples) | low transition | high transition |
|---|---|---|---|---|---|---|---|
| analysis data | 0.10 | 30.00 | fir | zero | 33793 | auto | auto |
| ICA copy (fit only) | 1.00 | 30.00 | fir | zero | 3381 | — | — |
- Resampled to 256.0 Hz — after the low-pass so nothing above the new Nyquist can alias, and before epoching so events become sample indices only once
- Recorded passband after filtering: 0.10–30.0 Hz
- highpass: 0.1 Hz: high enough to remove the drift of a 405-s recording, low enough not to distort the P3's late positivity (L2.4, pf-hp-cutoff-erp).
- lowpass: 30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains and the muscle band out without touching it. No notch is needed behind a 30 Hz low-pass.
- resample: 1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz is still eight times the low-pass. Resampling happens before epoching so events are converted once.
- ica_highpass: 1 Hz for the ICA fit only: drift dominates the variance and costs components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).
- ICA copy: ICA is fitted on a 1 Hz high-passed copy because slow drift dominates the variance and costs components; the unmixing is applied to the analysis data (L2.4)
- filtered on continuous data, so edge artifacts sit at the ends of the recording rather than inside every epoch (L2.4)
ICA components removed
infomax, 29 components, fitted on 1 Hz high-passed copy, seed 20260917, 500 iterations, converged: no. Components requested: rank carried through the pipeline: 30 EEG channels - 0 interpolated - reference cost = 29.
| component | class | score | ICLabel | evidence |
|---|---|---|---|---|
| IC8 | eye blink | 0.97 | eye blink p=0.971 | ICLabel classified it eye blink with probability 0.97 (threshold 0.8) |
| IC3 | muscle artifact | 0.96 | muscle artifact p=0.964 | ICLabel classified it muscle artifact with probability 0.96 (threshold 0.8) |
| IC27 | muscle artifact | 0.94 | muscle artifact p=0.944 | ICLabel classified it muscle artifact with probability 0.94 (threshold 0.8) |
| IC10 | eye blink | 0.82 | eye blink p=0.82 | ICLabel classified it eye blink with probability 0.82 (threshold 0.8) |
| IC0 | eye | 0.66 | eye blink p=0.986 | |correlation| with HEOG_left, HEOG_right, VEOG_lower = 0.66, above the configured threshold 0.5 (measure: correlation) |
5 component(s) removed; rank after cleaning 24.
Data rejected, per condition
| condition | epochs before | kept | rejected | % rejected |
|---|---|---|---|---|
| target | 40 | 38 | 2 | 5.00 |
| standard | 160 | 153 | 7 | 4.38 |
- Method:
threshold - Criterion (the same for every condition): peak-to-peak 100.0 µV, flat 1.0 µV
- Overall: 4.5% of epochs rejected; per-condition spread 0.6 percentage points
- Reasons recorded by MNE: PO8: 7, P8: 3, CPz: 2, P7: 2, PO7: 2, Fp1: 1, C3: 1, P4: 1
Figures
Run log
| step | status | seconds | what it recorded |
|---|---|---|---|
| load | ok | 0.30 | {"dataset": "ds-erpcore", "duration_s": 467.0, "n_channels": 33, "sfreq_hz": 1024.0} |
| montage | ok | 0.01 | {"n_positioned": 30} |
| bad_channels | ok | 0.14 | {"bads": []} |
| filter | ok | 1.20 | {"resampled_to_hz": 256.0, "sfreq_out_hz": 256.0} |
| interpolate | skipped | 0.00 | {"reason": "no bad channels to interpolate"} |
| reference | ok | 0.02 | {"n_channels": 33, "rank_after": 29, "type": "average"} |
| ica | ok | 12.06 | {"n_excluded": 5, "rank_after": 24} |
| epoch | ok | 0.05 | {"n_epochs": 200, "n_epochs_per_condition": {"standard": 160, "target": 40}, "sfreq_hz": 256.0} |
| reject | ok | 0.04 | {"percent_rejected": 4.5} |
The full run log (JSON) — every parameter as resolved, every note
{
"subject": "sub-001",
"dataset": "ds-erpcore",
"config_name": "erpcore-p3",
"config_hash": "17f71c09f4cf",
"seed": 20260917,
"status": "ok",
"error": null,
"traceback": null,
"started_at": "2026-09-18T14:22:11+00:00",
"duration_s": 13.819,
"versions": {
"python": "3.13.5",
"platform": "Darwin 25.3.0 (arm64)",
"mne": "1.10.2",
"numpy": "2.1.3",
"scipy": "1.15.3",
"matplotlib": "3.10.6",
"scikit-learn": "1.6.1",
"autoreject": "0.5.0",
"mne-icalabel": "0.9.0",
"python-picard": null,
"PyYAML": "6.0.2",
"pinned": {
"mne": "1.10.2",
"numpy": ">=2.1,<3",
"scipy": ">=1.15,<2"
},
"on_mismatch": "warn"
},
"version_mismatches": [],
"bad_channels": {},
"interpolated": [],
"rank": 24,
"filter": {
"sfreq_in_hz": 1024.0,
"ica_copy": {
"l_freq_hz": 1.0,
"h_freq_hz": 30.0,
"filter_length_samples": 3381,
"filter_length_s": 3.3018,
"why": "ICA is fitted on a 1 Hz high-passed copy because slow drift dominates the variance and costs components; the unmixing is applied to the analysis data (L2.4)"
},
"analysis": {
"l_freq_hz": 0.1,
"h_freq_hz": 30.0,
"method": "fir",
"phase": "zero",
"fir_design": "firwin",
"fir_window": "hamming",
"l_trans_bandwidth": "auto",
"h_trans_bandwidth": "auto",
"filter_length_samples": 33793,
"filter_length_s": 33.001
},
"resampled_to_hz": 256.0,
"resample_note": "after the low-pass so nothing above the new Nyquist can alias, and before epoching so events become sample indices only once",
"info_highpass_hz": 0.1,
"info_lowpass_hz": 30.0,
"sfreq_out_hz": 256.0,
"justification": {
"highpass": "0.1 Hz: high enough to remove the drift of a 405-s recording, low enough not to distort the P3's late positivity (L2.4, pf-hp-cutoff-erp).\n",
"lowpass": "30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains and the muscle band out without touching it. No notch is needed behind a 30 Hz low-pass.\n",
"resample": "1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz is still eight times the low-pass. Resampling happens before epoching so events are converted once.\n",
"ica_highpass": "1 Hz for the ICA fit only: drift dominates the variance and costs components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).\n"
},
"edge_note": "filtered on continuous data, so edge artifacts sit at the ends of the recording rather than inside every epoch (L2.4)"
},
"ica": {
"method": "infomax",
"fit_params": {
"extended": true
},
"seed": 20260917,
"n_components": 29,
"n_components_requested": 29,
"rank_why": "rank carried through the pipeline: 30 EEG channels - 0 interpolated - reference cost = 29",
"fitted_on": "1 Hz high-passed copy",
"n_iterations": 500,
"converged": false,
"excluded": [
{
"index": 8,
"label": "eye blink",
"score": 0.971,
"reason": "ICLabel classified it eye blink with probability 0.97 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "eye blink",
"probability": 0.971
}
},
{
"index": 3,
"label": "muscle artifact",
"score": 0.964,
"reason": "ICLabel classified it muscle artifact with probability 0.96 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "muscle artifact",
"probability": 0.964
}
},
{
"index": 27,
"label": "muscle artifact",
"score": 0.944,
"reason": "ICLabel classified it muscle artifact with probability 0.94 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "muscle artifact",
"probability": 0.944
}
},
{
"index": 10,
"label": "eye blink",
"score": 0.82,
"reason": "ICLabel classified it eye blink with probability 0.82 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "eye blink",
"probability": 0.82
}
},
{
"index": 0,
"label": "eye",
"score": 0.664,
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}
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"applied": true,
"rank_before": 29,
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"other",
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"brain",
"brain",
"brain",
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"brain",
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"brain",
"other",
"other",
"brain",
"brain",
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11,
22,
33,
44,
55
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9,
15,
21,
27
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12,
13,
14,
15,
21,
23,
24,
25,
31,
32,
34,
35,
41,
42,
43,
45,
51,
52,
53,
54
],
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3,
4,
5,
8,
10,
11,
12,
13,
14,
16,
17,
18,
19,
20,
22,
23,
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25,
26
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"subject": "sub-001",
"paradigm": "P3",
"name": "ERP CORE (Compendium of Open Resources and Experiments)",
"license": "CC BY 4.0 (OSF node thsqg; TODO(confirm): the component's own LICENSE file says CC BY-SA 4.0 and its dataset_description.json says CC0 -- the author reconciles this, spec section 10.11 item 8)",
"source": "https://osf.io/thsqg/",
"source_doi": "TODO(confirm)",
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"access": "open",
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{
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{
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{
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{
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"raw": ".../sub-001/sub-001_desc-clean_raw.fif",
"epochs": ".../sub-001/sub-001_desc-clean_epo.fif"
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"seed": 20260917
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"subject": "sub-001",
"source": "ERP CORE (Compendium of Open Resources and Experiments)",
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"justification": {
"highpass": "0.1 Hz: high enough to remove the drift of a 405-s recording, low enough not to distort the P3's late positivity (L2.4, pf-hp-cutoff-erp).\n",
"lowpass": "30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains and the muscle band out without touching it. No notch is needed behind a 30 Hz low-pass.\n",
"resample": "1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz is still eight times the low-pass. Resampling happens before epoching so events are converted once.\n",
"ica_highpass": "1 Hz for the ICA fit only: drift dominates the variance and costs components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).\n"
}
},
"duration_s": 1.2026,
"notes": {
"sfreq_in_hz": 1024.0,
"ica_copy": {
"l_freq_hz": 1.0,
"h_freq_hz": 30.0,
"filter_length_samples": 3381,
"filter_length_s": 3.3018,
"why": "ICA is fitted on a 1 Hz high-passed copy because slow drift dominates the variance and costs components; the unmixing is applied to the analysis data (L2.4)"
},
"analysis": {
"l_freq_hz": 0.1,
"h_freq_hz": 30.0,
"method": "fir",
"phase": "zero",
"fir_design": "firwin",
"fir_window": "hamming",
"l_trans_bandwidth": "auto",
"h_trans_bandwidth": "auto",
"filter_length_samples": 33793,
"filter_length_s": 33.001
},
"resampled_to_hz": 256.0,
"resample_note": "after the low-pass so nothing above the new Nyquist can alias, and before epoching so events become sample indices only once",
"info_highpass_hz": 0.1,
"info_lowpass_hz": 30.0,
"sfreq_out_hz": 256.0,
"justification": {
"highpass": "0.1 Hz: high enough to remove the drift of a 405-s recording, low enough not to distort the P3's late positivity (L2.4, pf-hp-cutoff-erp).\n",
"lowpass": "30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains and the muscle band out without touching it. No notch is needed behind a 30 Hz low-pass.\n",
"resample": "1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz is still eight times the low-pass. Resampling happens before epoching so events are converted once.\n",
"ica_highpass": "1 Hz for the ICA fit only: drift dominates the variance and costs components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).\n"
},
"edge_note": "filtered on continuous data, so edge artifacts sit at the ends of the recording rather than inside every epoch (L2.4)"
},
"status": "ok",
"error": null
},
{
"name": "interpolate",
"params": {
"enabled": true,
"method": "spline",
"reset_bads": true,
"exclude": [],
"max_interpolate": 6
},
"duration_s": 0.0,
"notes": {
"reason": "no bad channels to interpolate",
"bads_left_marked": [],
"rank": 30
},
"status": "skipped",
"error": null
},
{
"name": "reference",
"params": {
"type": "average",
"channels": [],
"projection": false,
"add_channels": []
},
"duration_s": 0.0203,
"notes": {
"type": "average",
"projection": false,
"note": "the average reference is a subtraction of the mean over channels, so the data lose one rank; the ICA step is told",
"rank_before": 30,
"rank_after": 29,
"rank_cost": 1,
"n_channels": 33
},
"status": "ok",
"error": null
},
{
"name": "ica",
"params": {
"enabled": true,
"method": "infomax",
"fit_params": {
"extended": true
},
"n_components": "rank",
"max_iter": 500,
"decim": 3,
"seed": 20260917,
"reject_by_annotation": true,
"fit_on": "ica_copy",
"eog": {
"enabled": true,
"channels": [],
"measure": "correlation",
"threshold": 0.5
},
"muscle": {
"enabled": false,
"threshold": 0.8
},
"iclabel": {
"enabled": true,
"labels": [
"eye blink",
"muscle artifact",
"heart beat",
"line noise",
"channel noise"
],
"threshold": 0.8
},
"max_remove": 6,
"apply": true
},
"duration_s": 12.0616,
"notes": {
"method": "infomax",
"fit_params": {
"extended": true
},
"seed": 20260917,
"n_components": 29,
"n_components_requested": 29,
"rank_why": "rank carried through the pipeline: 30 EEG channels - 0 interpolated - reference cost = 29",
"fitted_on": "1 Hz high-passed copy",
"n_iterations": 500,
"converged": false,
"excluded": [
{
"index": 8,
"label": "eye blink",
"score": 0.971,
"reason": "ICLabel classified it eye blink with probability 0.97 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "eye blink",
"probability": 0.971
}
},
{
"index": 3,
"label": "muscle artifact",
"score": 0.964,
"reason": "ICLabel classified it muscle artifact with probability 0.96 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "muscle artifact",
"probability": 0.964
}
},
{
"index": 27,
"label": "muscle artifact",
"score": 0.944,
"reason": "ICLabel classified it muscle artifact with probability 0.94 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "muscle artifact",
"probability": 0.944
}
},
{
"index": 10,
"label": "eye blink",
"score": 0.82,
"reason": "ICLabel classified it eye blink with probability 0.82 (threshold 0.8)",
"detector": "mne-icalabel",
"iclabel": {
"label": "eye blink",
"probability": 0.82
}
},
{
"index": 0,
"label": "eye",
"score": 0.664,
"reason": "|correlation| with HEOG_left, HEOG_right, VEOG_lower = 0.66, above the configured threshold 0.5 (measure: correlation)",
"detector": "ICA.find_bads_eog(EOG channels in the recording)",
"iclabel": {
"label": "eye blink",
"probability": 0.986
}
}
],
"n_excluded": 5,
"kept_by_max_remove": [],
"applied": true,
"rank_before": 29,
"rank_after": 24,
"eog": {
"enabled": true,
"channels": [
"HEOG_left",
"HEOG_right",
"VEOG_lower"
],
"provenance": "EOG channels in the recording"
},
"iclabel": {
"enabled": true,
"labels": [
"eye blink",
"other",
"brain",
"muscle artifact",
"eye blink",
"brain",
"other",
"brain",
"eye blink",
"brain",
"eye blink",
"muscle artifact",
"eye blink",
"brain",
"brain",
"brain",
"brain",
"brain",
"brain",
"other",
"brain",
"other",
"other",
"brain",
"brain",
"other",
"other",
"muscle artifact",
"brain"
],
"threshold": 0.8,
"requirements": "extended infomax, 1-100 Hz, average reference (mne-icalabel's stated conditions)"
},
"warning": "ICA did not converge in 500 iterations; the components are whatever the algorithm had reached (logged, not raised)"
},
"status": "ok",
"error": null
},
{
"name": "epoch",
"params": {
"enabled": true,
"source": "annotations",
"stim_channel": null,
"conditions": {
"target": [
11,
22,
33,
44,
55
],
"standard": [
12,
13,
14,
15,
21,
23,
24,
25,
31,
32,
34,
35,
41,
42,
43,
45,
51,
52,
53,
54
]
},
"tmin": -0.2,
"tmax": 0.8,
"baseline": [
-0.2,
0.0
],
"decim": 1,
"detrend": null,
"reject_by_annotation": true,
"event_repeated": "error",
"on_missing": "warn",
"shift_s": 0.0
},
"duration_s": 0.0535,
"notes": {
"source": "annotations",
"events_in_recording": 402,
"event_codes_present": {
"11": 1,
"12": 2,
"13": 3,
"14": 4,
"15": 5,
"201": 6,
"202": 7,
"21": 8,
"22": 9,
"23": 10,
"24": 11,
"25": 12,
"31": 13,
"32": 14,
"33": 15,
"34": 16,
"35": 17,
"41": 18,
"42": 19,
"43": 20,
"44": 21,
"45": 22,
"51": 23,
"52": 24,
"53": 25,
"54": 26,
"55": 27
},
"conditions": {
"target": {
"requested": [
11,
22,
33,
44,
55
],
"codes": [
1,
9,
15,
21,
27
],
"not_found": [],
"n_events": 40
},
"standard": {
"requested": [
12,
13,
14,
15,
21,
23,
24,
25,
31,
32,
34,
35,
41,
42,
43,
45,
51,
52,
53,
54
],
"codes": [
2,
3,
4,
5,
8,
10,
11,
12,
13,
14,
16,
17,
18,
19,
20,
22,
23,
24,
25,
26
],
"not_found": [],
"n_events": 160
}
},
"n_epochs": 200,
"n_epochs_per_condition": {
"target": 40,
"standard": 160
},
"dropped_at_epoching": {},
"tmin_s": -0.2,
"tmax_s": 0.8,
"baseline_s": [
-0.2,
0.0
],
"decim": 1,
"sfreq_hz": 256.0,
"trigger_shift_s": 0.0,
"note": "epoching came after resampling and after every continuous-domain step (L2.8); epochs overlapping a BAD_ annotation were dropped here, before any rejection criterion was applied"
},
"status": "ok",
"error": null
},
{
"name": "reject",
"params": {
"method": "threshold",
"ptp_uv": 100.0,
"flat_uv": 1.0,
"eog_ptp_uv": null,
"max_rejected_fraction": 0.4,
"condition_balance_pp": 10.0,
"autoreject": {
"n_interpolate": [
1,
4
],
"consensus": [],
"cv": 4,
"n_jobs": 1
}
},
"duration_s": 0.0375,
"notes": {
"method": "threshold",
"n_epochs_before": 200,
"criterion": {
"peak_to_peak_uv": 100.0,
"flat_uv": 1.0,
"eog_peak_to_peak_uv": null
},
"criterion_note": "one criterion for every condition, set before the data were looked at (pf-condition-biased-rejection)",
"per_condition": {
"target": {
"n_before": 40,
"n_kept": 38,
"n_rejected": 2,
"percent_rejected": 5.0
},
"standard": {
"n_before": 160,
"n_kept": 153,
"n_rejected": 7,
"percent_rejected": 4.38
}
},
"n_epochs_after": 191,
"percent_rejected": 4.5,
"condition_imbalance_pp": 0.62,
"drop_reasons": {
"CPz": 2,
"Fp1": 1,
"P7": 2,
"PO7": 2,
"P8": 3,
"PO8": 7,
"C3": 1,
"P4": 1,
"Fp2": 1,
"PO3": 1,
"Oz": 1
},
"flags": []
},
"status": "ok",
"error": null
}
],
"config": {
"name": "erpcore-p3",
"description": "Capstone C2 on ERP CORE P3: raw -> cleaned continuous data + epochs + a QC report per subject, in the canonical order of lesson L2.8, driven entirely by this file.\n",
"seed": 20260917,
"dataset": {
"id": "ds-erpcore",
"paradigm": "P3",
"runs": [],
"path": null,
"cache_dir": null,
"synthetic": {}
},
"subjects": {
"include": [
"sub-001"
],
"exclude": {},
"limit": null
},
"output": {
"dir": ".../T/nb-2-8-o9hlqr4r",
"overwrite": true,
"write_raw": true,
"write_epochs": true,
"write_log": true,
"write_qc": true,
"figures": true
},
"versions": {
"on_mismatch": "warn",
"pins": {
"mne": "1.10.2",
"numpy": ">=2.1,<3",
"scipy": ">=1.15,<2"
}
},
"steps": {
"load": {
"preload": true,
"eog_channels": [
"VEOG",
"HEOG"
],
"misc_channels": [],
"rename": {
"FP1": "Fp1",
"FP2": "Fp2"
},
"crop_s": null,
"bad_segments_s": []
},
"montage": {
"name": "standard_1005",
"match_case": true,
"on_missing": "warn"
},
"bad_channels": {
"enabled": true,
"window_s": 5.0,
"flat_uv": 1.0,
"flat_fraction": 0.5,
"deviation_z": 5.0,
"correlation_threshold": 0.4,
"correlation_fraction": 0.3,
"hf_noise_z": 5.0,
"manual": [],
"keep_existing": true,
"max_bad_fraction": 0.2
},
"filter": {
"l_freq": 0.1,
"h_freq": 30.0,
"method": "fir",
"phase": "zero",
"fir_design": "firwin",
"fir_window": "hamming",
"l_trans_bandwidth": "auto",
"h_trans_bandwidth": "auto",
"iir_params": {},
"notch_freqs": [],
"notch_widths": null,
"resample_hz": 256.0,
"ica_l_freq": 1.0,
"ica_h_freq": null,
"justification": {
"highpass": "0.1 Hz: high enough to remove the drift of a 405-s recording, low enough not to distort the P3's late positivity (L2.4, pf-hp-cutoff-erp).\n",
"lowpass": "30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains and the muscle band out without touching it. No notch is needed behind a 30 Hz low-pass.\n",
"resample": "1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz is still eight times the low-pass. Resampling happens before epoching so events are converted once.\n",
"ica_highpass": "1 Hz for the ICA fit only: drift dominates the variance and costs components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).\n"
}
},
"interpolate": {
"enabled": true,
"method": "spline",
"reset_bads": true,
"exclude": [],
"max_interpolate": 6
},
"reference": {
"type": "average",
"channels": [],
"projection": false,
"add_channels": []
},
"ica": {
"enabled": true,
"method": "infomax",
"fit_params": {
"extended": true
},
"n_components": "rank",
"max_iter": 500,
"decim": 3,
"seed": null,
"reject_by_annotation": true,
"fit_on": "ica_copy",
"eog": {
"enabled": true,
"channels": [],
"measure": "correlation",
"threshold": 0.5
},
"muscle": {
"enabled": false,
"threshold": 0.8
},
"iclabel": {
"enabled": true,
"labels": [
"eye blink",
"muscle artifact",
"heart beat",
"line noise",
"channel noise"
],
"threshold": 0.8
},
"max_remove": 6,
"apply": true
},
"epoch": {
"enabled": true,
"source": "annotations",
"stim_channel": null,
"conditions": {
"target": [
11,
22,
33,
44,
55
],
"standard": [
12,
13,
14,
15,
21,
23,
24,
25,
31,
32,
34,
35,
41,
42,
43,
45,
51,
52,
53,
54
]
},
"tmin": -0.2,
"tmax": 0.8,
"baseline": [
-0.2,
0.0
],
"decim": 1,
"detrend": null,
"reject_by_annotation": true,
"event_repeated": "error",
"on_missing": "warn",
"shift_s": 0.0
},
"reject": {
"method": "threshold",
"ptp_uv": 100.0,
"flat_uv": 1.0,
"eog_ptp_uv": null,
"max_rejected_fraction": 0.4,
"condition_balance_pp": 10.0,
"autoreject": {
"n_interpolate": [
1,
4
],
"consensus": [],
"cv": 4,
"n_jobs": 1
}
}
}
}
}Provenance, versions and seed
| dataset fact | value |
|---|---|
| name | ERP CORE (Compendium of Open Resources and Experiments) |
| license | CC BY 4.0 (OSF node thsqg; TODO(confirm): the component's own LICENSE file says CC BY-SA 4.0 and its dataset_description.json says CC0 -- the author reconciles this, spec section 10.11 item 8) |
| source | https://osf.io/thsqg/ |
| source_doi | TODO(confirm) |
| citation | Kappenman, Farrens, Zhang, Stewart & Luck (2021), ERP CORE |
| sfreq_hz | 1024.00 |
| reference | CMS (Biosemi ActiveTwo online reference) |
| hardware_filters | none recorded (BIDS eeg.json SoftwareFilters: n/a) |
| mains_hz | 60 |
| files | sub-001_task-P3_events.tsv, sub-001_task-P3_eeg.json, sub-001_task-P3_channels.tsv, sub-001_task-P3_electrodes.tsv, sub-001_task-P3_coordsystem.json, sub-001_task-P3_eeg.set, sub-001_task-P3_eeg.fdt |
Seed 20260917, configuration hash 17f71c09f4cf.
| package | in use | pinned in the config |
|---|---|---|
| python | 3.13.5 | — |
| platform | Darwin 25.3.0 (arm64) | — |
| mne | 1.10.2 | 1.10.2 |
| numpy | 2.1.3 | >=2.1,<3 |
| scipy | 1.15.3 | >=1.15,<2 |
| matplotlib | 3.10.6 | — |
| scikit-learn | 1.6.1 | — |
| autoreject | 0.5.0 | — |
| mne-icalabel | 0.9.0 | — |
| python-picard | not installed | — |
| PyYAML | 6.0.2 | — |
The resolved configuration (YAML) — this is what reproduces the run
name: erpcore-p3
description: 'Capstone C2 on ERP CORE P3: raw -> cleaned continuous data + epochs
+ a QC report per subject, in the canonical order of lesson L2.8, driven entirely
by this file.
'
seed: 20260917
dataset:
id: ds-erpcore
paradigm: P3
runs: []
path: null
cache_dir: null
synthetic: {}
subjects:
include:
- sub-001
exclude: {}
limit: null
output:
dir: .../T/nb-2-8-o9hlqr4r
overwrite: true
write_raw: true
write_epochs: true
write_log: true
write_qc: true
figures: true
versions:
on_mismatch: warn
pins:
mne: 1.10.2
numpy: '>=2.1,<3'
scipy: '>=1.15,<2'
steps:
load:
preload: true
eog_channels:
- VEOG
- HEOG
misc_channels: []
rename:
FP1: Fp1
FP2: Fp2
crop_s: null
bad_segments_s: []
montage:
name: standard_1005
match_case: true
on_missing: warn
bad_channels:
enabled: true
window_s: 5.0
flat_uv: 1.0
flat_fraction: 0.5
deviation_z: 5.0
correlation_threshold: 0.4
correlation_fraction: 0.3
hf_noise_z: 5.0
manual: []
keep_existing: true
max_bad_fraction: 0.2
filter:
l_freq: 0.1
h_freq: 30.0
method: fir
phase: zero
fir_design: firwin
fir_window: hamming
l_trans_bandwidth: auto
h_trans_bandwidth: auto
iir_params: {}
notch_freqs: []
notch_widths: null
resample_hz: 256.0
ica_l_freq: 1.0
ica_h_freq: null
justification:
highpass: '0.1 Hz: high enough to remove the drift of a 405-s recording, low
enough not to distort the P3''s late positivity (L2.4, pf-hp-cutoff-erp).
'
lowpass: '30 Hz: the P3 is a slow component, and 30 Hz keeps the 60 Hz mains
and the muscle band out without touching it. No notch is needed behind a 30
Hz low-pass.
'
resample: '1024 Hz costs time and disk for no gain at these cutoffs; 256 Hz
is still eight times the low-pass. Resampling happens before epoching so events
are converted once.
'
ica_highpass: '1 Hz for the ICA fit only: drift dominates the variance and costs
components; the unmixing is applied to the 0.1 Hz analysis data (L2.4, L2.6).
'
interpolate:
enabled: true
method: spline
reset_bads: true
exclude: []
max_interpolate: 6
reference:
type: average
channels: []
projection: false
add_channels: []
ica:
enabled: true
method: infomax
fit_params:
extended: true
n_components: rank
max_iter: 500
decim: 3
seed: null
reject_by_annotation: true
fit_on: ica_copy
eog:
enabled: true
channels: []
measure: correlation
threshold: 0.5
muscle:
enabled: false
threshold: 0.8
iclabel:
enabled: true
labels:
- eye blink
- muscle artifact
- heart beat
- line noise
- channel noise
threshold: 0.8
max_remove: 6
apply: true
epoch:
enabled: true
source: annotations
stim_channel: null
conditions:
target:
- 11
- 22
- 33
- 44
- 55
standard:
- 12
- 13
- 14
- 15
- 21
- 23
- 24
- 25
- 31
- 32
- 34
- 35
- 41
- 42
- 43
- 45
- 51
- 52
- 53
- 54
tmin: -0.2
tmax: 0.8
baseline:
- -0.2
- 0.0
decim: 1
detrend: null
reject_by_annotation: true
event_repeated: error
on_missing: warn
shift_s: 0.0
reject:
method: threshold
ptp_uv: 100.0
flat_uv: 1.0
eog_ptp_uv: null
max_rejected_fraction: 0.4
condition_balance_pp: 10.0
autoreject:
n_interpolate:
- 1
- 4
consensus: []
cv: 4
n_jobs: 1
Generated by eegpipe (pipelines/eegpipe) for the course capstone C2.
Cite a run by its configuration name and hash, the seed and the package versions above.
5. Two runs differ only by their configuration¶
One parameter is changed — the analysis high-pass moves from 0.1 Hz to 1 Hz, which L2.4 says costs a P3 a third of its amplitude — and nothing else. The two configuration hashes and the two run logs are the whole record of the difference. No file was edited to produce the second run.
In [9]:
if EEGPIPE is not None:
config_b = EEGPIPE.load_config(CONFIG_PATH, overrides={**OVERRIDES, "steps.filter.l_freq": 1.0,
"output.dir": str(OUT / "run-b")})
result_b = norm(EEGPIPE.run_subject(config_b, "sub-001"))
else:
import dataclasses
config_b = dataclasses.replace(config, l_freq=1.0)
result_b = norm(l2.run_subject(config_b, "sub-001", verbose=False))
rows = []
for label, res in (("A: analysis high-pass 0.1 Hz", R), ("B: analysis high-pass 1.0 Hz", result_b)):
fa = (res["filter"].get("analysis") or res["filter"])
rows.append({"run": label, "config hash": res["config_hash"],
"high-pass (Hz)": fa.get("l_freq_hz", float("nan")),
"FIR length (s)": fa.get("filter_length_s", res["filter"].get("fir_length_s", float("nan"))),
"bads interpolated": res["interpolated"] or ["-"], "rank": res["rank"],
"ICA removed": len(res["ica"]["removed"]),
"% rejected (all)": [r for r in res["rejection"]["table"] if r["condition"] == "all"][0]["percent_rejected"],
"imbalance (pp)": res["rejection"]["imbalance_pp"],
"duration (s)": res["duration_s"]})
print(l2.fmt_table(rows, list(rows[0]), floatfmt="{:.2f}"))
print()
print("Everything a reader needs in order to know why the two runs differ is the pair of configuration "
"hashes. That is what 'two runs differ only by their configuration' buys, and it is the test L2.8 "
"gives: could you reproduce last month's result by checking out the configuration file?")
In [10]:
# The measurement itself, so that the difference is a number rather than a claim.
CH, WINDOW = l2.P3_CHANNEL, l2.P3_WINDOW
measure_rows = []
def epochs_of(res_obj):
"""The cleaned epochs, whichever implementation produced them."""
if hasattr(res_obj, "status"):
if getattr(res_obj, "epochs", None) is not None:
return res_obj.epochs
p = (res_obj.outputs or {}).get("epochs")
return mne.read_epochs(p, verbose=False) if p and Path(p).exists() else None
return res_obj.get("epochs")
ep_a = epochs_of(raw_result)
ep_b_src = (EEGPIPE.run_subject(config_b, "sub-001") if EEGPIPE is not None
else l2.run_subject(config_b, "sub-001", verbose=False))
ep_b = epochs_of(ep_b_src)
fig, ax = plt.subplots(figsize=(9, 4.2))
for ep, label, color in ((ep_a, "A: high-pass 0.1 Hz", "tab:blue"), (ep_b, "B: high-pass 1.0 Hz", "tab:red")):
if ep is None:
continue
d = l2.difference_wave(ep)
amp = l2.mean_amplitude(d, CH, WINDOW)
measure_rows.append({"run": label, f"{CH} mean (uV)": amp, "trials": int(d.nave)})
ax.plot(d.times * 1000, d.data[d.ch_names.index(CH)] * 1e6, lw=1.6, color=color,
label=f"{label}: {amp:+.2f} uV")
ax.axvspan(WINDOW[0] * 1000, WINDOW[1] * 1000, color="tab:orange", alpha=0.18, label="a-priori window")
ax.axhline(0, color="gray", lw=0.6); ax.axvline(0, color="gray", lw=0.6)
ax.set(xlabel="Time from stimulus (ms)", ylabel="Amplitude (uV)",
title=f"sub-001: target minus standard at {CH} under the two configurations (uV, positive up)")
ax.legend(fontsize=8); ax.grid(alpha=0.3)
fig.tight_layout()
plt.show() # render the static figure(s) of this cell inline
print(l2.fmt_table(measure_rows, list(measure_rows[0]) if measure_rows else [], floatfmt="{:+.2f}"))
6. A failure is a row, not a stopped cohort¶
A subject whose file is missing, whose events cannot be read, or whose ICA does not converge must appear in the report as exactly that. It must not raise out of the loop and end the run at subject 7 of 40.
In [11]:
bad = run_one(config, "sub-999") if EEGPIPE is None else run_one(config, "sub-999", progress=False)
B = norm(bad)
print(f"ok = {B['ok']}")
print(f"error recorded: {str(B['error'])[:200]}")
print(f"the run log still exists, with {len(B['steps'])} step(s):")
print(l2.fmt_table([{"step": s["name"], "duration (s)": s["duration_s"],
"notes": str(s.get("notes", ""))[:70]} for s in B["steps"]]
or [{"step": "(none completed)", "duration (s)": 0.0, "notes": ""}],
["step", "duration (s)", "notes"], floatfmt="{:.2f}"))
qc_bad = B["outputs"].get("qc")
if qc_bad and Path(qc_bad).exists():
print("\nand the QC report renders, saying what happened:")
display(HTML(Path(qc_bad).read_text(encoding="utf-8")))
else:
print("\nand the QC report renders, saying what happened:")
display(HTML(l2.qc_report_html(bad, title="QC report -- sub-999 (failed)")
if EEGPIPE is None else f"<p><b>sub-999 failed:</b> {B['error']}</p>"))
7. The numbers¶
In [12]:
print("nb-2-8-pipeline -- L2.8 numbers (draft; TODO(confirm) at author review)")
print(f"Implementation: " + (f"pipelines/eegpipe {EEGPIPE.__version__}" if EEGPIPE is not None
else "helpers_l2.run_subject (the documented interface; pipelines/eegpipe absent)"))
print(f"Configuration: " + (str(CONFIG_PATH.relative_to(_pipelines.parent)) if EEGPIPE is not None
else "helpers_l2.PipelineConfig defaults")
+ f", hash {R['config_hash']}, seed {R['seed']}")
print(f"Data: ds-erpcore P3 sub-001 (CC BY-SA 4.0 per data/directory.yaml, contested at source, open, per-subject downloadable).")
print(f"Canonical order executed: "
+ (" -> ".join(EEGPIPE.CANONICAL_ORDER) if EEGPIPE is not None
else "load -> montage -> bad-channel detection -> filter -> interpolate -> re-reference -> ICA -> "
"epoch -> reject"))
print()
print("Run log (step, duration, notes):")
for s in R["steps"]:
print(f" {s['name']:<16s} {s['duration_s']:6.2f} s {str(s.get('notes', ''))[:96]}")
print(f" {'TOTAL':<16s} {R['duration_s']:6.2f} s")
print()
print(f"Bad channels: {R['interpolated'] or 'none'} interpolated of {len(R['bads'])} flagged "
f"({', '.join(f'{ch}: {c}' for ch, c in R['bads'].items()) or 'nothing flagged'})")
print(f"Rank: {R['rank']} -- {R['rank_why']}; after ICA cleaning {R['ica']['rank_after']}")
fa = R["filter"].get("analysis") or R["filter"]
print(f"Filter as resolved (analysis branch): {fa.get('l_freq_hz')}-{fa.get('h_freq_hz')} Hz, "
f"{fa.get('method', '?')}, phase {fa.get('phase', '?')}, "
f"{fa.get('filter_length_samples', fa.get('fir_length_samples', '?'))} taps = "
f"{fa.get('filter_length_s', fa.get('fir_length_s', float('nan'))):.1f} s")
if isinstance(R["filter"].get("ica_copy"), dict):
ic = R["filter"]["ica_copy"]
print(f"Filter as resolved (ICA branch): {ic.get('l_freq_hz')}-{ic.get('h_freq_hz')} Hz, "
f"{ic.get('filter_length_samples', '?')} taps = {ic.get('filter_length_s', float('nan')):.1f} s")
print(f"ICA: {R['ica']['method']}, {R['ica']['n_components']} components, seed {R['ica']['seed']}, "
f"fitted on {R['ica']['fitted_on']}; {len(R['ica']['removed'])} removed:")
for r in R["ica"]["removed"]:
print(f" IC{r['component']:<3} {str(r['class']):16s} score {r['probability']:.3f} {r['evidence'][:96]}")
print("Rejection:")
for r in R["rejection"]["table"]:
print(f" {str(r['condition']):9s} presented {r['n_presented']:3d} rejected {r['n_rejected']:3d} "
f"kept {r['n_kept']:3d} ({r['percent_rejected']:.2f} %)")
print(f" per-condition imbalance {R['rejection']['imbalance_pp']:.2f} pp; criterion "
f"{R['rejection']['criterion']}")
if measure_rows:
print()
print("Measured P3 under the two configurations (the only difference is the analysis high-pass):")
for m in measure_rows:
print(f" {m['run']:26s} {CH} mean amplitude {m[f'{CH} mean (uV)']:+5.2f} uV "
f"({m['trials']} trials)")
print()
print(f"Versions recorded in the run log: {R['versions']}")
print()
print("L2.8's exercises are an ordering exercise (ex-2-8-pipeline-order; key: load, montage, bad-channel "
"detection, filter, interpolate, re-reference, ICA, epoch, reject) and a free response; this notebook "
"produces no numeric key. The QC report in section 4 is the C2 deliverable in miniature.")
_out.cleanup()
print("\ntemporary output directory removed; the ERP CORE downloads stay in the cache.")