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doc/api/statistics.rst

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.. autosummary::
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:toctree: ../generated/
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erp.compute_area
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erp.compute_frac_area_latency
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erp.compute_frac_peak_latency
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erp.compute_peak
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erp.compute_sme
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Compute ``adjacency`` matrices for cluster-level statistics:
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Add ``rng`` parameters to APIs that use randomness, while retaining keyword-only ``seed`` and ``random_state`` for compatibility (:gh:`9233` by `Bruno Aristimunha`_).
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Added ``meanlogratio`` baselining mode to :meth:`mne.time_frequency.EpochsTFR.apply_baseline` and related functions, by :newcontrib:`Virginie van Wassenhove`.
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Added 4 new ERP measures: peak latency, peak amplitude, fractional peak latency, and fractional area latency. By `Jacob Woessner`_ and :newcontrib:`Beliz Sertcan Gökmen`.
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The auto-picked peak vertex and its activity trace in the :class:`mne.viz.Brain` GUI now follow the active overlay, by `Payam Sadeghi-Shabestari`_.

doc/changes/names.inc

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.. _Baris Talar: https://github.com/baris-talar
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.. _Basile Pinsard: https://github.com/bpinsard
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.. _Beige Jerry Jin: https://github.com/BeiGeJin
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.. _Beliz Sertcan Gökmen: https://github.com/BelizSertcan
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.. _Ben Beasley: https://github.com/musicinmybrain
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.. _Ben Tang: https://github.com/bentang18
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.. _Benedikt Ehinger: https://www.benediktehinger.de
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.. _Victor Férat: https://github.com/vferat
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.. _Victoria Peterson: https://github.com/vpeterson
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.. _Vincent Gao: https://github.com/gaoflow
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.. _Virginie van Wassenhove: https://brainthemind.com/virginie-van-wassenhove
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.. _Wei Xu: https://github.com/psyxw
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.. _Will Turner: https://bootstrapbill.github.io
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.. _Wouter Kroot: https://github.com/WouterKroot

doc/references.bib

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note = {hal-01848442}
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}
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@article{KinleyEtAl2026,
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author={Kinley, Isaac and Roberts, Reece P and Meltzer, Jed A and Addis, Donna Rose},
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title={Spectral change or Jensen gap? Log-ratio baseline correction for time-frequency M/EEG is negatively biased},
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journal={Journal of Neuroscience Methods},
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pages={110826},
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year={2026},
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publisher={Elsevier}
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}
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@article{KnuutilaEtAl1993,
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author = {Knuutila, Jukka E. T. and Ahonen, Antti I. and Hämäläinen, Matti S. and Kajola, Matti J. and Laine, P. P. and Lounasmaa, Olli V. and Parkkonen, Lauri T. and Simola, Juha T. A. and Tesche, Claudia D.},
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doi = {10.1109/20.281163},

examples/datasets/spm_faces_dataset.py

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raw.resample(100)
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raw.filter(1.0, None) # high-pass
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reject = dict(mag=5e-12)
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ica = ICA(n_components=0.95, max_iter="auto", random_state=0)
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ica = ICA(n_components=0.95, max_iter="auto", rng=97)
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ica.fit(raw, reject=reject)
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# compute correlation scores, get bad indices sorted by score
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eog_epochs = create_eog_epochs(raw, ch_name="MRT31-2908", reject=reject)

examples/decoding/decoding_csp_eeg.py

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scores = []
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epochs_data = epochs.get_data(copy=False)
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epochs_data_train = epochs_train.get_data(copy=False)
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cv = ShuffleSplit(10, test_size=0.2, random_state=42)
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cv = ShuffleSplit(10, test_size=0.2, random_state=103)
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cv_split = cv.split(epochs_data_train)
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# Assemble a classifier

examples/decoding/decoding_csp_timefreq.py

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LinearDiscriminantAnalysis(),
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)
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n_splits = 3 # for cross-validation, 5 is better, here we use 3 for speed
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cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)
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cv = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=191)
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# Classification & time-frequency parameters
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tmin, tmax = -0.200, 2.000

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