Note

Go to the end to download the full example code or to run this example in your browser via Binder.

Predict observed age (years) from real EEG features#

Estimated reading time:8 minutes

Evaluate a Ridge baseline with one held-out participant at a time.

Use six explicitly selected participants from the real HBN ds005505 release. Their RestingState signal files total approximately 595.8 MB, cached under EEGDASH_CACHE_DIR. This applied example is larger than the introductory 21 MB SSVEP subset. Cropping after loading reduces computation, not download. Targets come from the observed participant metadata. Six participants are sufficient to exercise the workflow, not to support clinical conclusions.

Before you start#

Install EEGDash with its EEGPrep tutorial dependencies and scikit-learn. Tutorials 02, 11 and 40 introduce windows, grouped evaluation and spectral features. This file runs independently: it predicts age in years from one feature row per participant and prints each held-out prediction.

import os
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from braindecode.preprocessing import (
    create_fixed_length_windows,
    preprocess,
    Resampling,
    RemoveDrifts,
)

from eegdash import EEGDashDataset
from functools import partial
from eegdash.features import (
    FeatureExtractor,
    extract_features,
    spectral_bands_power,
    spectral_preprocessor,
)
from sklearn.dummy import DummyRegressor
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import LeaveOneOut
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

1. Load the named cohort and inspect observed participant metadata#

age is an observed participant attribute in years, not a target inferred from EEG or assigned according to recording order. Its numerical range in this small cohort defines where the model is being tested; it cannot establish performance at ages not represented here.

The two assertions require one selected recording per named participant. description_fields makes the source attributes available alongside BIDS identifiers; the EEG still comes from EEGDashDataset. These are the original OpenNeuro recordings, not the separately filtered/downsampled challenge release supplied by EEGChallengeDataset.

subjects = [
    "NDARBH024NH2",
    "NDARAM704GKZ",
    "NDARAC904DMU",
    "NDARAN385MDH",
    "NDARAG143ARJ",
    "NDARAP359UM6",
]
cache_dir = Path(os.environ.get("EEGDASH_CACHE_DIR", ".eegdash_cache"))
dataset = EEGDashDataset(
    dataset="ds005505",
    task="RestingState",
    subject=subjects,
    cache_dir=cache_dir,
    description_fields=["subject", "task", "age", "sex", "p_factor"],
    n_jobs=1,
)
assert len(dataset.datasets) == len(subjects)
assert dataset.description["subject"].nunique() == len(subjects)
print(dataset.description[["subject", "age", "sex", "p_factor"]])

╭────────────────────── EEG 2025 Competition Data Notice ──────────────────────╮
│ This notice is only for users who are participating in the EEG 2025          │
│ Competition.                                                                 │
│                                                                              │
│ EEG 2025 Competition Data Notice!                                            │
│ You are loading one of the datasets that is used in competition, but via     │
│ `EEGDashDataset`.                                                            │
│                                                                              │
│ IMPORTANT:                                                                   │
│ If you download data from `EEGDashDataset`, it is NOT identical to the       │
│ official                                                                     │
│ competition data, which is accessed via `EEGChallengeDataset`. The           │
│ competition data has been downsampled and filtered.                          │
│                                                                              │
│ If you are participating in the competition,                                 │
│ you must use the `EEGChallengeDataset` object to ensure consistency.         │
│                                                                              │
│ If you are not participating in the competition, you can ignore this         │
│ message.                                                                     │
╰─────────────────────────── Source: EEGDashDataset ───────────────────────────╯
        subject      age sex  p_factor
0  NDARAC904DMU  11.3386   F    -0.603
1  NDARAG143ARJ   7.6648   M    -0.258
2  NDARAM704GKZ  10.9449   M     0.062
3  NDARAN385MDH  10.7089   M     0.034
4  NDARAP359UM6  12.8422   F     0.467
5  NDARBH024NH2   8.6883   F    -0.818

2. Prepare the first minute of recorded EEG#

The fixed first-minute interval and four named electrodes bound computation and establish a common channel order. They are not selected by test accuracy. The source reference channel can be flat; the chosen channels exclude that reference rather than pretending a zero-variance channel can be standardized.

Braindecode’s EEGPrep Resampling adapter reduces the rate to 100 Hz, with anti-alias filtering, before RemoveDrifts applies its high-pass transition from 0.5 to 1 Hz. Resampling first reduces the later filter’s computation. These published components run on each recording separately; no custom filtering function or cross-participant fit is needed.

A two-second window then contains 200 voltage samples. The source reference is retained: we do not average-reference an arbitrary four-channel subset. EEGPrep’s forward-backward drift filter is offline, not causal streaming preprocessing. This small recipe neither detects every artifact nor invokes channel rejection or ASR. The feature models below use only 1–30 Hz.

Fixed-length windows are used because the target belongs to a person, not an event. They do not relabel eyes-open/closed intervals or remove every instruction or artifact from the first minute. Equal size and stride avoid overlap; a final short remainder is discarded. EEGDash reads the windows in batches shaped (windows, 4 channels, 200 samples). Check the printed window count rather than assuming each recording retains the same number.

channels = ["E11", "E62", "E75", "E22"]
for recording in dataset.datasets:
    raw = recording.raw
    print(
        recording.description["subject"],
        raw.info["sfreq"],
        raw.ch_names,
        "observed annotations:",
        sorted(set(raw.annotations.description)),
    )
    assert set(channels).issubset(raw.ch_names)
    raw.crop(tmax=59.99).load_data().pick(channels).reorder_channels(channels)
# Preserve annotation times in seconds across EEGPrep format conversions.
# Retain the measurement date too: it anchors annotations with absolute times.
annotations_before = [
    recording.raw.annotations.copy() for recording in dataset.datasets
]
measurement_dates = [recording.raw.info["meas_date"] for recording in dataset.datasets]
durations_before = [
    recording.raw.n_times / recording.raw.info["sfreq"]
    for recording in dataset.datasets
]
preprocess(
    dataset, [Resampling(sfreq=100), RemoveDrifts(transition=(0.5, 1.0))], n_jobs=1
)
for recording, annotations, duration, measurement_date in zip(
    dataset.datasets, annotations_before, durations_before, measurement_dates
):
    raw = recording.raw
    assert raw.ch_names == channels and raw.info["sfreq"] == 100
    assert abs(raw.n_times / 100 - duration) <= 1 / 100
    raw.set_meas_date(measurement_date)
    raw.set_annotations(annotations)
    assert raw.annotations.orig_time == annotations.orig_time
    np.testing.assert_array_equal(raw.annotations.onset, annotations.onset)
    np.testing.assert_array_equal(raw.annotations.description, annotations.description)
windows = create_fixed_length_windows(
    dataset,
    window_size_samples=200,
    window_stride_samples=200,
    on_last_window="drop",
    preload=True,
)
metadata = windows.get_metadata().reset_index(drop=True)
groups = metadata["subject"].astype(str).to_numpy()
assert len(windows) == len(metadata)
assert not metadata.duplicated(["subject", "i_start_in_trial"]).any()
print("Real two-second windows:", len(windows), "with", len(channels), "channels")

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NDARAC904DMU 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['break cnt', 'dot_no1_OFF', 'dot_no1_ON', 'dot_no2_OFF', 'dot_no2_ON', 'dot_no3_OFF', 'dot_no3_ON', 'dot_no4_OFF', 'dot_no4_ON', 'dot_no5_OFF', 'dot_no5_ON', 'dot_no6_OFF', 'dot_no6_ON', 'dot_no7_OFF', 'dot_no7_ON', 'dot_no8_OFF', 'dot_no8_ON', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'learningBlock_1', 'resting_start', 'seqLearning_start']

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NDARAG143ARJ 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['boundary', 'break cnt', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'resting_start']

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NDARAM704GKZ 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['boundary', 'break cnt', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'resting_start']

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NDARAN385MDH 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['boundary', 'break cnt', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'resting_start']

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NDARAP359UM6 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['boundary', 'break cnt', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'resting_start']

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NDARBH024NH2 500.0 ['E1', 'E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9', 'E10', 'E11', 'E12', 'E13', 'E14', 'E15', 'E16', 'E17', 'E18', 'E19', 'E20', 'E21', 'E22', 'E23', 'E24', 'E25', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E32', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39', 'E40', 'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E48', 'E49', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E56', 'E57', 'E58', 'E59', 'E60', 'E61', 'E62', 'E63', 'E64', 'E65', 'E66', 'E67', 'E68', 'E69', 'E70', 'E71', 'E72', 'E73', 'E74', 'E75', 'E76', 'E77', 'E78', 'E79', 'E80', 'E81', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E88', 'E89', 'E90', 'E91', 'E92', 'E93', 'E94', 'E95', 'E96', 'E97', 'E98', 'E99', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E107', 'E108', 'E109', 'E110', 'E111', 'E112', 'E113', 'E114', 'E115', 'E116', 'E117', 'E118', 'E119', 'E120', 'E121', 'E122', 'E123', 'E124', 'E125', 'E126', 'E127', 'E128', 'Cz'] observed annotations: ['boundary', 'break cnt', 'instructed_toCloseEyes', 'instructed_toOpenEyes', 'resting_start']
/tmp/tmpk1mq_oa9.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmp9slpbca0.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmp4zqkao8e.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmpwbqk0suh.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmpp4b3438x.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmp2g65tkm6.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmp5_xec79e.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmposmq6lj5.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmp0_cwnocb.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmphk4dcfik.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmpgdvqskx2.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
/tmp/tmpzku7755w.set
Field 'subject' is missing from the EEG dictionnary, adding it.
Field 'group' is missing from the EEG dictionnary, adding it.
Field 'condition' is missing from the EEG dictionnary, adding it.
Field 'session' is missing from the EEG dictionnary, adding it.
Field 'comments' is missing from the EEG dictionnary, adding it.
Field 'times' is missing from the EEG dictionnary, adding it.
Field 'icaact' is missing from the EEG dictionnary, adding it.
Field 'icachansind' is missing from the EEG dictionnary, adding it.
Field 'urchanlocs' is missing from the EEG dictionnary, adding it.
Field 'urevent' is missing from the EEG dictionnary, adding it.
Field 'eventdescription' is missing from the EEG dictionnary, adding it.
Field 'epoch' is missing from the EEG dictionnary, adding it.
Field 'epochdescription' is missing from the EEG dictionnary, adding it.
Field 'stats' is missing from the EEG dictionnary, adding it.
Field 'specdata' is missing from the EEG dictionnary, adding it.
Field 'specicaact' is missing from the EEG dictionnary, adding it.
Field 'splinefile' is missing from the EEG dictionnary, adding it.
Field 'icasplinefile' is missing from the EEG dictionnary, adding it.
Field 'dipfit' is missing from the EEG dictionnary, adding it.
Field 'history' is missing from the EEG dictionnary, adding it.
Field 'saved' is missing from the EEG dictionnary, adding it.
Field 'etc' is missing from the EEG dictionnary, adding it.
Field 'datfile' is missing from the EEG dictionnary, adding it.
Field 'run' is missing from the EEG dictionnary, adding it.
Field 'roi' is missing from the EEG dictionnary, adding it.
Real two-second windows: 180 with 4 channels

3. Average window band powers into one feature row per participant#

EEGDash’s FeatureExtractor shares one Welch spectrum between the four band-power outputs. nperseg=200 spans the two-second window, giving a 0.5 Hz grid; four bands times four electrodes produce 16 named columns. spectral_bands_power sums PSD values in each band. Its scale depends on that fixed grid, so preserve the rate and segment length when reusing this feature definition. No hand-written FFT or band-reduction function is needed.

Log compression acts on each trial’s feature values. We then group the resulting table by subject and average within each person before fitting. Thus X has six rows, and no participant gains weight merely because more windows were retained. The same sorted index selects the observed participant targets, preserving feature/target alignment.

bands = {"delta": (1, 4), "theta": (4, 8), "alpha": (8, 13), "beta": (13, 30)}
spectral = FeatureExtractor(
    {"power": partial(spectral_bands_power, bands=bands)},
    preprocessor=partial(
        spectral_preprocessor, fs=100, nperseg=200, noverlap=0, f_min=1, f_max=30
    ),
)
feature_table = extract_features(
    windows, spectral, batch_size=64, n_jobs=1
).to_dataframe()
assert feature_table.shape == (len(metadata), len(bands) * len(channels))
participant_features = (
    np.log10(feature_table.clip(lower=1e-30))
    .assign(subject=groups)
    .groupby("subject")
    .mean()
)
identities = participant_features.index.to_numpy()
X = participant_features.to_numpy()
participants = dataset.description.set_index("subject").loc[identities]
assert np.isfinite(X).all() and len(X) == len(subjects)

y = pd.to_numeric(participants["age"], errors="raise").to_numpy(dtype=float)
assert np.isfinite(y).all()

Extracting features:   0%|          | 0/6 [00:00<?, ?it/s]
Extracting features: 100%|██████████| 6/6 [00:00<00:00, 333.59it/s]

4. Fit only on training participants and measure held-out errors#

Each leave-one-out fold has five training people and one test person. StandardScaler is fitted anew on the five training feature rows. Ridge uses a fixed alpha=10 penalty to limit large coefficients in a setting with more features than training participants; it is not a tuned optimum. The separate mean predictor uses only those same training targets.

Mean absolute error is the average absolute participant prediction error, measured in years. Lower is better. Compare the model’s MAE with the training-mean baseline rather than a classification chance line. The baseline can outperform the EEG model, and no assertion demands otherwise. The table lets you see whether the average error is driven by one individual; the diagonal in the scatter plot marks perfect prediction, not a fitted line.

predicted, baseline = np.empty_like(y), np.empty_like(y)
for train, test in LeaveOneOut().split(X):
    assert set(identities[train]).isdisjoint(identities[test])
    model = make_pipeline(StandardScaler(), Ridge(alpha=10))
    predicted[test] = model.fit(X[train], y[train]).predict(X[test])
    baseline[test] = DummyRegressor().fit(X[train], y[train]).predict(X[test])
print(
    pd.DataFrame(
        {
            "subject": identities,
            "observed": y,
            "predicted": predicted,
            "training_mean": baseline,
        }
    )
)
print("Participant MAE:", mean_absolute_error(y, predicted))
print("Training-mean MAE:", mean_absolute_error(y, baseline))
fig, ax = plt.subplots(figsize=(5, 4), layout="constrained")
ax.scatter(y, predicted, label="Held-out participants")
ax.plot([y.min(), y.max()], [y.min(), y.max()], "k--")
ax.set(xlabel="Observed age (years)", ylabel="Predicted age (years)")
ax.legend()
plt.show()

        subject  observed  predicted  training_mean
0  NDARAC904DMU   11.3386   9.661879       10.16982
1  NDARAG143ARJ    7.6648  10.689279       10.90458
2  NDARAM704GKZ   10.9449   9.625699       10.24856
3  NDARAN385MDH   10.7089   8.128444       10.29576
4  NDARAP359UM6   12.8422  14.781776        9.86910
5  NDARBH024NH2    8.6883  11.057284       10.69988
Participant MAE: 2.151569673156738
Training-mean MAE: 1.7504533333333336

What this participant split can establish#

The six held-out predictions exercise a leakage-safe analysis, but their folds share training participants and are not six independent experiments. A narrow age range, short recording segment or acquisition confound can strongly influence this small result. Add participants and define nested validation before comparing feature sets or regularization settings.

Total running time of the script: (0 minutes 10.701 seconds)