Note
Go to the end to download the full example code or to run this example in your browser via Binder.
Eyes-open versus eyes-closed decoding on a cluster#
Estimated reading time:12 minutes
Load actual HBN ds005514 RestingState recordings with EEGDashDataset,
reannotate observed open/close instructions, and train a neural classifier.
The default selects three explicit, previously inspected subject IDs;
all selected subjects must load successfully. The three signal files total
281.2 MB plus sidecars. See HBN ds005514. Inspect sizes before staging.
This is a full EO/EC workload, not the small real-data CI smoke test.
From the repository root, submit sbatch examples/hpc/run_eoec_cpu.slurm
after configuring the cluster account/partition and Python environment.
Use EEGDASH_CACHE_DIR for persistent or pre-staged node-local cache,
NUM_SUBJECTS=3, NUM_TEST_SUBJECTS=1 and SEED=42 by default.
The test subjects remain untouched until the fixed training schedule ends.
The single split is a workflow demonstration, not a population benchmark.
Prerequisites: tutorial 02’s DataLoader, tutorial 11’s subject split and
basic PyTorch training. Install the project dependencies, including a
PyTorch build appropriate for the compute node, plus EEGPrep and its
EEGLAB reader (pip install 'eegprep[eeglabio]>=0.2.23,<0.3'). A CPU can run the default
cohort; the GPU Slurm template requests a GPU but training selects CUDA only
when PyTorch can access one. Cluster accounts and partitions are site-specific.
The deliverables are an actual voltage-window plot and a final held-out accuracy in the job log. The script does not save a reusable trained model; write model state and the preprocessing/subject configuration explicitly if a later inference job needs them.
from pathlib import Path
import os
import numpy as np
import torch
os.environ.setdefault("NUMBA_DISABLE_JIT", "1")
os.environ.setdefault("MNE_USE_NUMBA", "false")
os.environ.setdefault("_MNE_FAKE_HOME_DIR", str(Path.cwd()))
(Path(os.environ["_MNE_FAKE_HOME_DIR"]) / ".mne").mkdir(exist_ok=True)
from eegdash import EEGDashDataset
from eegdash.paths import get_default_cache_dir
from braindecode.preprocessing import (
preprocess,
Preprocessor,
RemoveDCOffset,
RemoveDrifts,
Resampling,
create_windows_from_events,
)
from eegdash.hbn.preprocessing import hbn_ec_ec_reannotation
1. Configure the bounded real-data workload#
cache_folder = get_default_cache_dir()
cache_folder.mkdir(parents=True, exist_ok=True)
dataset_id = "ds005514"
task = "RestingState"
# Explicit upper bound; failed acquisitions stop the job.
num_subjects = int(os.environ.get("NUM_SUBJECTS", "3"))
num_test_subjects = int(os.environ.get("NUM_TEST_SUBJECTS", "1"))
random_state = int(os.environ.get("SEED", "42"))
# 2 seconds at 128 Hz
window_size_samples = 256
# training params
epochs = int(os.environ.get("EPOCHS", "6"))
batch_size = int(os.environ.get("BATCH_SIZE", "32"))
2. Define preprocessing and observed-instruction reannotation#
The HBN-specific preprocessor derives windows from recorded instructions. For eyes-closed it starts two-second segments 15–27 seconds after the close cue; for eyes-open it starts them 5–17 seconds after the open cue. This avoids immediate cue transitions. The labels indicate instructed state rather than an independent measurement that a participant complied.
We keep a fixed 24-channel montage subset so the neural model sees identical sensor ordering. EEGPrep RemoveDCOffset subtracts the per-channel median; RemoveDrifts applies a fixed forward/backward high-pass transition from 0.5 to 1 Hz. EEGPrep Resampling reduces the rate to 128 Hz with anti-aliasing. A final 55 Hz low-pass avoids frequencies near the new 64 Hz Nyquist limit. These deterministic per-recording operations use no population-fitted state. Original instruction times in seconds are restored after verifying that resampling preserved the time origin and duration within one output sample. Reannotation then creates state markers on the resampled grid. This is component-level EEGPrep preprocessing, not automatic ASR/artifact rejection.
preprocessors = [
Preprocessor(
"pick_channels",
ch_names=[
"E22",
"E9",
"E33",
"E24",
"E11",
"E124",
"E122",
"E29",
"E6",
"E111",
"E45",
"E36",
"E104",
"E108",
"E42",
"E55",
"E93",
"E58",
"E52",
"E62",
"E92",
"E96",
"E70",
"Cz",
],
),
RemoveDCOffset(),
RemoveDrifts(transition=(0.5, 1.0)),
Resampling(sfreq=128),
Preprocessor("filter", l_freq=None, h_freq=55),
]
/home/runner/work/EEGDash/EEGDash/.venv/lib/python3.12/site-packages/braindecode/preprocessing/preprocess.py:78: UserWarning: apply_on_array can only be True if fn is a callable function. Automatically correcting to apply_on_array=False.
warn(
3. Load the selected participants and create real EO/EC windows#
SUBJECTS is an explicit comma-separated cohort. NUM_SUBJECTS selects a prefix of that list and must not exceed it. Increase both settings when adding participants; the script never searches for replacements after an error. The default subjects were inspected for both instruction labels and the requested channel names. Inspect those contracts again for a new cohort.
subjects_all = os.environ.get(
"SUBJECTS", "NDARAE710YWG,NDARAH239PGG,NDARAL897CYV"
).split(",")
assert len(set(subjects_all)) == len(subjects_all)
all_windows = []
all_subject_ids = []
valid_subjects = []
assert 2 <= num_subjects <= len(subjects_all)
assert 1 <= num_test_subjects < num_subjects
selected_subjects = subjects_all[:num_subjects]
print("Selected subjects:", selected_subjects)
for subj in selected_subjects:
ds_eoec = EEGDashDataset(
dataset=dataset_id,
task=task,
subject=subj,
cache_dir=cache_folder,
)
print(ds_eoec.description)
original_annotations = []
original_timing = []
for recording in ds_eoec.datasets:
raw = recording.raw
original_annotations.append(raw.annotations.copy())
original_timing.append(
(raw.first_time, raw.n_times / raw.info["sfreq"], raw.info["meas_date"])
)
print(raw.ch_names, raw.info["sfreq"], np.unique(raw.annotations.description))
assert {"instructed_toCloseEyes", "instructed_toOpenEyes"}.issubset(
raw.annotations.description
)
preprocess(ds_eoec, preprocessors)
# These EEGPrep components never remove time. Preserve source cue times
# across EEGLAB/MNE conversion rather than accepting sample-origin shifts.
for recording, annotations, (origin, duration, measurement_date) in zip(
ds_eoec.datasets, original_annotations, original_timing, strict=True
):
raw = recording.raw
assert abs(raw.first_time - origin) <= 1 / raw.info["sfreq"]
assert abs(raw.n_times / raw.info["sfreq"] - duration) <= 1 / raw.info["sfreq"]
raw.set_meas_date(measurement_date)
raw.set_annotations(annotations)
np.testing.assert_allclose(
raw.annotations.onset, annotations.onset, atol=1 / raw.info["sfreq"], rtol=0
)
np.testing.assert_array_equal(
raw.annotations.description, annotations.description
)
preprocess(ds_eoec, [hbn_ec_ec_reannotation()])
# Each derived annotation is a point marker. The stop offset extends it by
# 256 samples (two seconds after resampling), while the matching size/stride
# creates one full window per marker. This differs from a source annotation
# that already carries a nonzero trial duration: do not add the same offset
# blindly to another dataset.
windows_ds = create_windows_from_events(
ds_eoec,
mapping={"eyes_closed": 0, "eyes_open": 1},
trial_start_offset_samples=0,
trial_stop_offset_samples=window_size_samples,
window_size_samples=window_size_samples,
window_stride_samples=window_size_samples,
on_last_window="drop",
preload=True,
)
n_win = len(windows_ds)
assert n_win > 0, f"No valid windows for {subj}"
assert set(windows_ds.get_metadata().target) == {0, 1}
print("Windows for subject:", n_win)
all_windows.append(windows_ds)
all_subject_ids.extend([subj] * n_win)
valid_subjects.append(subj)
if len(valid_subjects) < 2:
raise RuntimeError(
f"Only {len(valid_subjects)} valid subject(s) collected; need >=2."
)
if num_test_subjects >= len(valid_subjects):
raise ValueError("NUM_TEST_SUBJECTS must be < number of valid subjects found.")
print("\nUsing valid subjects:", valid_subjects)
print("Total subjects requested:", num_subjects, " | collected:", len(valid_subjects))
# Concatenate
from braindecode.datasets import BaseConcatDataset
concat_ds = BaseConcatDataset(all_windows)
print("Total windows across valid subjects:", len(concat_ds))
Selected subjects: ['NDARAE710YWG', 'NDARAH239PGG', 'NDARAL897CYV']
╭────────────────────── 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 task age sex ... symbolsearch session run gender
0 NDARAE710YWG RestingState 9.986 M ... caution None None None
[1 rows x 28 columns]
['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'] 500.0 ['boundary' 'break cnt' 'instructed_toCloseEyes' 'instructed_toOpenEyes'
'resting_start']
/tmp/tmpuj_8o_7h.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.
[09/16/26 21:07:49] WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
/tmp/tmplppdppwp.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/tmpnahjh6hy.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.
Windows for subject: 70
╭────────────────────── 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 task age sex ... symbolsearch session run gender
0 NDARAH239PGG RestingState 7.7552 M ... available None None None
[1 rows x 28 columns]
['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'] 500.0 ['boundary' 'break cnt' 'instructed_toCloseEyes' 'instructed_toOpenEyes'
'resting_start']
/tmp/tmp067uzi8l.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.
[09/16/26 21:07:51] WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
/tmp/tmp7pw975vy.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/tmplfaqu9jv.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.
Windows for subject: 70
╭────────────────────── 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 task age sex ... symbolsearch session run gender
0 NDARAL897CYV RestingState 12.9895 M ... available None None None
[1 rows x 28 columns]
['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'] 500.0 ['boundary' 'break cnt' 'instructed_toCloseEyes' 'instructed_toOpenEyes'
'resting_start']
/tmp/tmp9e22hfr1.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.
[09/16/26 21:07:53] WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
WARNING EEGPrep event count eegprep_preprocess.py:123
changed during
RemoveDCOffset processing
(14 annotated events, 13
non-boundary events);
restoring durations in
order for the overlapping
subset only.
/tmp/tmprt3n87ky.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/tmpfbcujqec.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.
Windows for subject: 70
Using valid subjects: ['NDARAE710YWG', 'NDARAH239PGG', 'NDARAL897CYV']
Total subjects requested: 3 | collected: 3
Total windows across valid subjects: 210
4. Inspect one recorded window without a display server#
The saved trace is channel zero of a real window, in volts against sample index. It checks waveform extraction, not class separability. Divide the horizontal indices by 128 when interpreting elapsed seconds.
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
if len(concat_ds) > 2:
plt.figure()
plt.plot(concat_ds[2][0][0, :].transpose())
plt.savefig("sample_epoch.png", dpi=150, bbox_inches="tight")
print("Saved plot to sample_epoch.png")
Saved plot to sample_epoch.png
5. Keep the test participant outside training#
Shuffle participant IDs once, not windows across participants. The default seed assigns one person to test and two to train; all windows from each person stay together. Multiple windows from the same instructed block are correlated, so window count is not the number of independent participants.
rng = np.random.RandomState(random_state)
subjects_shuffled = valid_subjects.copy()
rng.shuffle(subjects_shuffled)
test_subjects = set(subjects_shuffled[:num_test_subjects])
train_subjects = set(subjects_shuffled[num_test_subjects:])
assert train_subjects.isdisjoint(test_subjects)
print("\nTrain subjects:", sorted(train_subjects))
print("Test subjects :", sorted(test_subjects))
indices = np.arange(len(concat_ds))
subj_arr = np.array(all_subject_ids)
train_indices = indices[np.isin(subj_arr, list(train_subjects))]
test_indices = indices[np.isin(subj_arr, list(test_subjects))]
print("Train windows:", len(train_indices), "Test windows:", len(test_indices))
Train subjects: ['NDARAH239PGG', 'NDARAL897CYV']
Test subjects : ['NDARAE710YWG']
Train windows: 140 Test windows: 70
6. Build training and test batches#
Each input has axes (windows, 24 channels, 256 samples) and starts in volts. The default cohort has 140 training and 70 test windows. Labels are integer class indices: zero for eyes-closed, one for eyes-open. A label mean of 0.5 means equal counts, not an accuracy result. Only training batches shuffle. Materializing arrays is practical here; a much larger cohort should use indexed datasets/lazy loaders rather than duplicating every window in RAM.
torch.manual_seed(random_state)
np.random.seed(random_state)
X_train = torch.FloatTensor(np.array([concat_ds[i][0] for i in train_indices]))
X_test = torch.FloatTensor(np.array([concat_ds[i][0] for i in test_indices]))
y_train = torch.LongTensor(np.array([concat_ds[i][1] for i in train_indices]))
y_test = torch.LongTensor(np.array([concat_ds[i][1] for i in test_indices]))
from torch.utils.data import DataLoader, TensorDataset
assert torch.isfinite(X_train).all() and torch.isfinite(X_test).all()
assert set(y_train.tolist()) == set(y_test.tolist()) == {0, 1}
dataset_train = TensorDataset(X_train, y_train)
dataset_test = TensorDataset(X_test, y_test)
train_loader = DataLoader(dataset_train, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset_test, batch_size=batch_size, shuffle=False)
print(
f"X_train {X_train.shape} | Train batches: {len(train_loader)} | Test batches: {len(test_loader)}"
)
print(
f"Label balance train: {float(y_train.float().mean()):.2f} | test: {float(y_test.float().mean()):.2f}"
)
X_train torch.Size([140, 24, 256]) | Train batches: 5 | Test batches: 3
Label balance train: 0.50 | test: 0.50
7. Match the neural model to the window contract#
ShallowFBCSPNet combines temporal/spatial filtering with pooled power-like features. The constructor specifies 24 channels, two output scores and 256 time samples. final_conv_length=”auto” derives the final temporal kernel from that input length; changing windows requires changing n_times.
from torch.nn import functional as F
from braindecode.models import ShallowFBCSPNet
model = torch.nn.Sequential(
torch.nn.LayerNorm(256, eps=1e-14, elementwise_affine=False),
ShallowFBCSPNet(24, 2, n_times=256, final_conv_length="auto"),
)
8. Train for a fixed schedule and reserve the final evaluation#
Adamax uses a fixed learning rate of 0.002 and weight decay of 0.001. Six epochs are an inexpensive workflow check, not a tuned convergence claim. Changing these choices after reading test accuracy uses the test person for selection; instead reserve validation people inside the training cohort.
optimizer = torch.optim.Adamax(model.parameters(), lr=0.002, weight_decay=0.001)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device=device)
print("Using epochs =", epochs, "| device =", device, "| batch_size =", batch_size)
Using epochs = 6 | device = cpu | batch_size = 32
Normalization is per window and channel, along the time axis. The model therefore consumes dimensionless standardized signals. Native LayerNorm divides by sqrt(population variance + 1e-14 V²), with no learned affine parameters. This replaces a custom normalization helper and keeps division finite for nearly constant channels. This transform does not estimate a shared mean or variance across participants, so applying it to a test window does not expose other test windows to the trained model.
Each forward pass produces two class scores per window. Cross-entropy compares them with the observed integer target; backpropagation updates only the training model. model.train() enables training behavior such as dropout and BatchNorm updates. The final model.eval() and no_grad() disable those updates and gradient tracking for the reserved participant.
for e in range(epochs):
model.train()
correct_train = 0.0
for x, y in train_loader:
scores = model(x.to(device=device, dtype=torch.float32))
y = y.to(device=device, dtype=torch.long)
loss = F.cross_entropy(scores, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
preds = scores.argmax(dim=1)
correct_train += (preds == y).sum().item()
print(f"Epoch {e}, train accuracy: {correct_train / len(dataset_train):.3f}")
# Evaluate the held-out subjects once after the fixed training schedule.
model.eval()
correct_test = 0
with torch.no_grad():
for x, y in test_loader:
scores = model(x.to(device=device, dtype=torch.float32))
correct_test += (scores.argmax(dim=1).cpu() == y).sum().item()
print(f"Final held-out subject accuracy: {correct_test / len(dataset_test):.3f}")
Epoch 0, train accuracy: 0.593
Epoch 1, train accuracy: 0.700
Epoch 2, train accuracy: 0.700
Epoch 3, train accuracy: 0.721
Epoch 4, train accuracy: 0.764
Epoch 5, train accuracy: 0.829
Final held-out subject accuracy: 0.571
9. Interpret the job outputs and plan validation#
Training accuracy is counted during parameter updates, so it is not a separate fixed-model evaluation. Final accuracy is the fraction of the reserved participant’s windows classified correctly. The default test labels are balanced; check the printed balance when changing the cohort before using 0.5 as a chance reference. Seventy windows from one person do not provide seventy independent estimates of population generalization.
If the training score rises while the held-out score stays low, investigate a validation split of additional training people before increasing epochs. For a research result, repeat subject-disjoint outer folds and report each participant’s score. Save configuration, logs and sample_epoch.png to persistent storage before job scratch is removed; a successful Slurm exit alone is not evidence that the classifier generalizes.
Total running time of the script: (0 minutes 6.319 seconds)
