"""
.. _ex-epoch-quality:

========================================
Exploring epoch quality before rejection
========================================

This example shows an approach for identifying epochs containing potential artifacts and
rejecting these bad epochs. We compute per-epoch outlier scores using peak-to-peak
amplitude, variance, and kurtosis — inspired by FASTER :footcite:`NolanEtAl2010` and
:footcite:t:`DelormeEtAl2007` — and use them to rank epochs from cleanest to noisiest to
inform rejection decisions.
"""
# Authors: Aman Srivastava
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

# %%
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import kurtosis

import mne
from mne.datasets import eegbci

print(__doc__)

# %%
# Load the EEGBCI dataset and create epochs
# -----------------------------------------
raw_fname = eegbci.load_data(subjects=3, runs=(3,))[0]
raw = mne.io.read_raw(raw_fname, preload=True)
eegbci.standardize(raw)
montage = mne.channels.make_standard_montage("standard_1005")
raw.set_montage(montage)

events, event_id = mne.events_from_annotations(raw)
epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.5, preload=True, baseline=(None, 0))

# %%
# Compute per-epoch outlier scores
# --------------------------------
# Peak-to-peak amplitude, variance, and kurtosis are computed per epoch. Each feature is
# z-scored robustly using median absolute deviation across epochs, and averaged into a
# single outlier score normalised between [0, 1]. Scores close to 1 indicate a likely
# presence of artifacts in the epoch.

data = epochs.get_data()  # (n_epochs, n_channels, n_times)

ptp = np.ptp(data, axis=-1).mean(axis=-1)
var = data.var(axis=-1).mean(axis=-1)
kurt = np.array([kurtosis(data[i].ravel()) for i in range(len(data))])

features = np.column_stack([ptp, var, kurt])
median = np.median(features, axis=0)
mad = np.median(np.abs(features - median), axis=0) + 1e-10
z = np.abs((features - median) / mad)

raw_score = z.mean(axis=-1)
scores = (raw_score - raw_score.min()) / (raw_score.max() - raw_score.min() + 1e-10)

# %%
# Determining outlier epochs
# --------------------------
# Below, epochs are ranked from cleanest to noisiest. We need to find an appropriate
# threshold to flag those epochs likely containing artifacts. The threshold to use will
# vary depending on the dataset and analysis goals. In the plot, we show two example
# thresholds: a more lenient threshold of 0.6; and a stricter threshold of 0.3.
fig, ax = plt.subplots(layout="constrained")
sorted_idx = np.argsort(scores)
ax.bar(np.arange(len(scores)), scores[sorted_idx], color="steelblue")
ax.axhline(0.6, color="red", linestyle="--", label="More lenient threshold (0.6)")
ax.axhline(0.3, color="orange", linestyle="--", label="Stricter threshold (0.3)")
ax.set(
    xlabel="Epoch (sorted by score)",
    ylabel="Outlier score",
    title="Epoch quality scores (0 = clean, 1 = likely artifact)",
)
ax.legend()

for threshold in [0.6, 0.3]:
    bad_epochs = np.where(scores > threshold)[0]
    print(
        f"Threshold {threshold}: {len(bad_epochs)} epochs flagged "
        f"out of {len(epochs)} total"
    )

# %%
# Epochs flagged by the thresholds can be inspected using the :meth:`mne.Epochs.plot`
# method. This is a crucial step in identifying the optimal threshold. First, we show
# those epochs with the worst scores (≥ 0.6), containing a number of amplitude spikes.
picks = np.arange(17, 40, dtype=int)  # channels with notable amplitude spikes
epochs[np.where(scores >= 0.6)[0]].plot(
    picks=picks, title="Scores ≥ 0.6", scalings=dict(eeg=70e-6), n_channels=len(picks)
)
# %%
# In contrast, the threshold of 0.3 captures epochs with less severe artifact activity,
# which may be overly conservative to exclude from the analysis.
epochs[np.where((scores >= 0.3) & (scores < 0.6))[0]].plot(
    picks=picks,
    title="0.3 ≤ scores < 0.6",
    scalings=dict(eeg=70e-6),
    n_channels=len(picks),
)

# %%
# Dropping suspicious epochs
# --------------------------
# Following visual inspection, bad epochs can be discarded using the
# :meth:`mne.Epochs.drop` method. Here, we remove the worst scoring epochs (≥ 0.6)
# which contained strong artifact activity. The remaining good epochs can then be used
# for further analysis.
#
# .. admonition:: Repairing and retaining epochs with artifacts
#
#     In this case, the large frontal deflections are likely blink artifacts, so
#     dropping is not the only option. For artifacts like these, it can be
#     preferable to repair the data and keep more epochs, for example with
#     :ref:`ICA <tut-artifact-ica>` or
#     :ref:`regression-based artifact correction <tut-artifact-regression>`.
epochs.drop(np.where(scores >= 0.6)[0])
print(f"Epochs remaining after dropping scores ≥ 0.6: {len(epochs)}")

# %%
# References
# ----------
# .. footbibliography::
