Fix the catalogue defects from the full-dataset sweep (#1142, #1143 items 1–3)

docs/source/api.rst

@@ -67,6 +67,7 @@ Motor Imagery Datasets
     BNCI2025_001
     BNCI2025_002
     Cho2017
+    Kaneshiro2015
     Dreyer2023
     Dreyer2023A
     Dreyer2023B
@@ -176,7 +177,6 @@ ERP/P300 Datasets
     Lee2021Mobile_ERP
     Chailloux2020
     GuttmannFlury2025_P300
-    Kaneshiro2015
     Lee2024_AC
     Lee2024_BS
     Lee2024_DL

docs/source/whats_new.rst

@@ -61,6 +61,10 @@ Requirements
 
 Bugs
 ~~~~
+- Fix :class:`moabb.datasets.BNCI2022_001` epoching its instantaneous waypoint markers as 90-second trials: only ``trajectory_start`` is a trial event now (32 bounded, non-overlapping 90 s epochs per subject), and the loader annotates each trigger pulse once (rising edge) instead of once per held sample, which had inflated subject 1 to 33,114 annotations and a 364 GiB allocation. Waypoint and trajectory-end markers stay available as annotations on the loaded raws (:gh:`1143` by `Bruno Aristimunha`_)
+- Retag :class:`moabb.datasets.Kaneshiro2015` from ``p300`` to ``imagery`` so its declared default paradigm accepts it: its six visual object categories carry no Target/NonTarget pair, so the P300 paradigm rejected every subject. The catalog row and documentation grouping move with it (:gh:`1143` by `Bruno Aristimunha`_)
+- Allow :class:`moabb.paradigms.RestingStateToP300Adapter` to be constructed with defaults: ``events=None`` now means all of the dataset's events, matching how every in-repo usage builds it (:gh:`1143` by `Bruno Aristimunha`_)
+- Fix :class:`moabb.datasets.Lee2024` silently dropping 345 upstream files. ``data_path`` no longer guesses filenames and skips failures: it downloads a real inventory -- the NEMAR deposit's provenance manifest when the sourcedata store is in use (so the NEMAR path never contacts the upstream host), or the upstream git tree otherwise -- through the shared :func:`moabb.datasets.download.data_dl`, which also serves the files from the NEMAR store. Subject 8's unpadded upstream names (``sub8_*``) are normalized to the padded names the loader reads, mixtures of combined and per-block training files and ``param.mat`` are covered by the inventory, and any download failure now raises instead of leaving a silently incomplete directory (:gh:`1142` by `Bruno Aristimunha`_)
 - Prefetch the NEMAR sourcedata store inside :meth:`moabb.datasets.base.BaseDataset.get_data`: the store introduced in :gh:`1146` was only filled by an explicit :meth:`~moabb.datasets.base.BaseDataset.download` call, so a plain ``get_data()`` on a fresh machine still fetched from the upstream host even with the provider pinned to ``"nemar"``. The requested subjects' ``sourcedata/`` is now fetched before loading, with the provider policy ``download`` already implements: ``"upstream"`` skips NEMAR, ``"nemar"`` treats a failure as fatal, ``"auto"`` warns per subject and leaves that subject to the dataset's own downloader. Also fix the store fill in the deprecated :func:`moabb.datasets.download.data_path`: ``pooch.retrieve`` treats its destination as a *directory* holding ``<md5(url)>-<basename>``, and loaders such as :class:`moabb.datasets.Rodrigues2017`'s ``os.listdir()`` it, so a store hit written as a plain file at the destination raised ``NotADirectoryError``; the hit now lands inside the wrapper directory under pooch's unique name (by `Bruno Aristimunha`_)
 - Wire the NEMAR sourcedata store into loading (:gh:`1147`): ``data_dl`` and the deprecated ``data_path`` now serve a requested file from the dataset's ``NEMAR/<nemar_id>/sourcedata/`` store before consulting the URL-derived layout, probing the store by the trailing segments of the URL path since it keeps the upstream filenames. ``dataset.download()`` followed by ``get_data()`` therefore no longer re-contacts the upstream host -- verified live against ``Schirrmeister2017``, whose host is currently unreachable. The URL-derived trees remain as lookups so nothing already downloaded is fetched again; ``force_update`` still refetches upstream and pinning the provider to ``"upstream"`` opts loading out of the store (:gh:`1146` by `Bruno Aristimunha`_)
 - Store :class:`moabb.datasets.ErpCore2021` as the single combined BIDS dataset it is, with components separated by the ``task-`` entity in one shared ``MNE-erpcore2021-data`` root, instead of seven standalone per-component BIDS datasets; likewise store Dreyer2023 in one shared ``MNE-dreyer2023-data`` root, since the A/B/C classes only select subject ranges of one globally numbered dataset. Pre-existing downloads in the legacy separated layouts are still read without re-fetching (:gh:`1146` by `Bruno Aristimunha`_)

moabb/datasets/bnci/bnci_2022_001.py

@@ -299,8 +299,11 @@ def _convert_run_001_2022(
             255: "trajectory_end",
         }
 
-        # Find non-zero trigger positions
-        event_indices = np.where(trigger != 0)[0]
+        # The trigger is sampled continuously, so a pulse spans several
+        # samples; annotate only its onset (rising edge or value change).
+        event_indices = np.flatnonzero(
+            (trigger != 0) & (np.diff(trigger, prepend=0) != 0)
+        )
         if len(event_indices) > 0:
             event_times = event_indices / sfreq
             event_values = trigger[event_indices].astype(int)
@@ -381,6 +384,15 @@ class BNCI2022_001(BNCIBaseDataset):
     - waypoint_hit (48): Drone successfully passed through waypoint
     - trajectory_end (255): End of trajectory (3s after final waypoint)
 
+    Only ``trajectory_start`` is declared as a trial event (``events``), matching
+    the ~90 second trajectory ``interval``: paradigms therefore epoch the 32
+    trajectories per subject. The waypoint and trajectory-end codes mark
+    instantaneous point events (roughly a thousand per subject), so epoching
+    them with the 90 s trial window is not meaningful. They are still annotated
+    on the raw data returned by the loader; to analyse them, epoch the raw
+    annotations directly or pass a custom ``process_pipeline`` to
+    :meth:`~moabb.datasets.base.BaseDataset.get_data`.
+
     **Data Organization**
 
     - 1 session per subject (offline data only, online sessions not included)
@@ -661,12 +673,9 @@ def __init__(self, subjects=None, sessions=None, *, return_all_modalities=False)
         super().__init__(
             subjects=list(range(1, 14)),
             sessions_per_subject=1,
-            events={
-                "trajectory_start": 1,
-                "waypoint_miss": 16,
-                "waypoint_hit": 48,
-                "trajectory_end": 255,
-            },
+            # Waypoint/end codes (16/48/255) are instantaneous point markers,
+            # not 90 s trials; see the class docstring.
+            events={"trajectory_start": 1},
             code="BNCI2022-001",
             interval=[0, 90],  # Approximately 90 seconds per trajectory
             paradigm="imagery",  # For compatibility

moabb/datasets/kaneshiro2015.py

@@ -98,7 +98,7 @@ class Kaneshiro2015(BaseDataset):
         ),
         experiment=ExperimentMetadata(
             events=dict(_EVENTS),
-            paradigm="p300",
+            paradigm="imagery",  # 6-class visual ERP, no Target/NonTarget
             n_classes=6,
             class_labels=list(_EVENTS.keys()),
             trial_duration=0.496,
@@ -143,7 +143,7 @@ def __init__(self, subjects=None, sessions=None):
             events=dict(_EVENTS),
             code="Kaneshiro2015",
             interval=[0, 0.496],
-            paradigm="p300",
+            paradigm="imagery",  # 6-class visual ERP, no Target/NonTarget
             doi=_DOI,
             selected_subjects=subjects,
             selected_sessions=sessions,

moabb/datasets/lee2024.py

@@ -5,6 +5,7 @@
 Data: https://github.com/jml226/Home-Appliance-Control-Dataset
 """
 
+import json
 import logging
 from functools import partialmethod
 from pathlib import Path
@@ -33,6 +34,10 @@
 _GITHUB_RAW = (
     "https://raw.githubusercontent.com/jml226/Home-Appliance-Control-Dataset/main"
 )
+_GITHUB_API_TREE = (
+    "https://api.github.com/repos/jml226/Home-Appliance-Control-Dataset"
+    "/git/trees/main?recursive=1"
+)
 _DOI = "10.3389/fnhum.2024.1320457"
 _SIGN = "lee2024erp"
 
@@ -438,57 +443,67 @@ def _build_raw(signals, trigger, config):
 
         return raw
 
+    _upstream_paths = None
+
+    @classmethod
+    def _upstream_tree(cls):
+        """Blob paths of the upstream repo -- fetched once per process."""
+        if cls._upstream_paths is None:
+            import requests
+
+            resp = requests.get(_GITHUB_API_TREE, timeout=120)
+            resp.raise_for_status()
+            cls._upstream_paths = [
+                e["path"] for e in resp.json()["tree"] if e["type"] == "blob"
+            ]
+        return cls._upstream_paths
+
+    def _subject_files(self, config, subj_str):
+        """One subject's files, as upstream-relative paths under the experiment.
+
+        The NEMAR deposit's provenance manifest is the preferred inventory --
+        it mirrors the upstream names exactly and is already local after the
+        sourcedata store is fetched, so the NEMAR path never contacts the
+        upstream host at all. Without a store (provider ``"upstream"``), the
+        upstream git tree is the authoritative inventory (gh-1142: guessing
+        filename patterns silently dropped 345 files, e.g. subject 8's
+        unpadded ``sub8_*`` names and every ``param.mat``).
+        """
+        store = self._sourcedata_store()
+        manifest = store / "sourcedata_provenance.json" if store else None
+        if manifest and manifest.is_file():
+            rels = (f["file"] for f in json.loads(manifest.read_text())["files"])
+        else:
+            prefix = config["dir_name"] + "/"
+            rels = (
+                path[len(prefix) :]
+                for path in self._upstream_tree()
+                if path.startswith(prefix)
+            )
+        return sorted(r for r in rels if r.startswith(f"Dat_{subj_str}/"))
+
     def data_path(
         self, subject, path=None, force_update=False, update_path=None, verbose=None
     ):
         if subject not in self.subject_list:
             raise ValueError("Invalid subject number")
 
         config = _EXPERIMENT_CONFIGS[self._experiment]
-        subj_dir = self._subject_dir(subject, path)
         subj_str = self._subj_str(subject, config)
-
-        import requests as _requests
-
-        files_to_dl = []
-
-        # Testing blocks.
-        for i in range(1, 31):
-            files_to_dl.append(f"{subj_str}_Testing{i}.mat")
-
-        # Training blocks (per-block).
-        if config["has_training"] and not config["training_combined"]:
-            for i in range(1, 51):
-                files_to_dl.append(f"{subj_str}_Training{i}.mat")
-
-        # Combined training.
-        if config["has_training"] and config["training_combined"]:
-            files_to_dl.append(f"{subj_str}_Training.mat")
-
-        # Calibration signal.
-        files_to_dl.append("cal_sig.mat")
-
-        subj_dir.mkdir(parents=True, exist_ok=True)
-
-        for fname in files_to_dl:
-            local = subj_dir / fname
-            if local.exists() and not force_update:
-                continue
-            dir_name = config["dir_name"]
-            url = f"{_GITHUB_RAW}/{dir_name}/Dat_{subj_str}/{fname}"
-            log.info("Downloading %s ...", fname)
-            try:
-                resp = _requests.get(url, stream=True, timeout=120)
-                if resp.status_code == 404:
-                    continue
-                resp.raise_for_status()
-                with open(local, "wb") as fout:
-                    for chunk in resp.iter_content(chunk_size=8192):
-                        fout.write(chunk)
-            except Exception as e:
-                log.warning("Download failed for %s: %s", fname, e)
-
-        return str(subj_dir)
+        unpadded = self._subj_str(subject, {"zero_pad": False})
+        for rel in self._subject_files(config, subj_str):
+            # The loader constructs zero-padded names; subject 8's upstream
+            # files are unpadded (sub8_*), so normalize the local name.
+            local = rel.replace(f"{unpadded}_", f"{subj_str}_")
+            dl.data_dl(
+                f"{_GITHUB_RAW}/{config['dir_name']}/{rel}",
+                _SIGN,
+                path=path,
+                force_update=force_update,
+                verbose=verbose,
+                fname=f"{config['dir_name']}/{local}",
+            )
+        return str(self._subject_dir(subject, path))
 
 
 class Lee2024_TV(Lee2024):

moabb/datasets/summary_imagery.csv

@@ -7,6 +7,7 @@ BNCI2014_004,9,3,2,360,4.5,250,5,1,32400
 BNCI2015_001,12,13,2,200,5.0,512,3,1,14400
 BNCI2015_004,9,30,5,80,7.0,256,2,1,7200
 Cho2017,52,64,2,100,3.0,512,1,1,9800
+Kaneshiro2015,10,124,6,864,0.496,62.5,1,1,5184
 Lee2019_MI,54,62,2,100,4.0,1000,2,1,11000
 GrosseWentrup2009,10,128,2,150,7.0,500,1,1,3000
 Schirrmeister2017,14,128,4,120,4.0,500,1,2,13440

moabb/datasets/summary_p300.csv

@@ -64,7 +64,6 @@ Lee2024_BS,14,31,varies NT / T,1.0,500,1
 Lee2024_AC,10,25,varies NT / T,1.0,500,1
 Zheng2020,14,62,168 T / 4032 NT,1.0,1000,2
 Zhang2025,15,57,varies T / NT,1.0,1000,4
-Kaneshiro2015,10,124,5184 (6 classes),0.496,62.5,1
 Simoes2020,15,8,varies NT / T,1.0,250,7
 Speier2017,10,32,~1200 per run,1.0,256,2
 BCIComp2020WalkingERP,15,46,240 NT / 60 T,1.0,100,1

moabb/paradigms/resting_state.py

@@ -72,7 +72,7 @@ def __init__(
         )
 
     def used_events(self, dataset):
-        return {ev: dataset.event_id[ev] for ev in self.events}
+        return {ev: dataset.event_id[ev] for ev in self.events or dataset.event_id}
 
     def is_valid(self, dataset):
         ret = True

moabb/tests/test_bnci.py

@@ -1,10 +1,12 @@
 from types import SimpleNamespace
 
 import numpy as np
+from scipy.io import savemat
 
-from moabb.datasets import BNCI2014_001, BNCI2014_008
+from moabb.datasets import BNCI2014_001, BNCI2014_008, BNCI2022_001
 from moabb.datasets.bnci.base import _BNCI_ARTIFACT_ANNOTATION_DESCRIPTION, _convert_run
-from moabb.datasets.preprocessing import _is_preserved_annotation
+from moabb.datasets.bnci.bnci_2022_001 import _convert_run_001_2022
+from moabb.datasets.preprocessing import SetRawAnnotations, _is_preserved_annotation
 
 
 def _fake_mi_run():
@@ -79,6 +81,74 @@ def test_bnci_artifact_markers_survive_event_rederivation():
         assert _is_preserved_annotation(description)
 
 
+def _fake_2022_001_mat(path, sfreq=128, n_trajectories=3, pulse_samples=13):
+    """Write a synthetic BNCI2022-001 task MAT file and return layout info.
+
+    Mimics the public release structure (``EEG``, ``EOG``, ``Trigger``,
+    ``Header``): each ~90 s trajectory starts with a trigger pulse of code 1,
+    contains 4 waypoint pulses (codes 48/16 alternating) and ends with a code
+    255 pulse. As in the real recordings, the hardware trigger holds each code
+    for several consecutive samples (``pulse_samples``).
+    """
+    traj_samples = 92 * sfreq  # ~90 s trajectory + 2 s gap
+    n_samples = n_trajectories * traj_samples
+    trigger = np.zeros(n_samples)
+    for k in range(n_trajectories):
+        t0 = k * traj_samples
+        trigger[t0 : t0 + pulse_samples] = 1
+        for w in range(4):
+            p = t0 + (w + 1) * 10 * sfreq
+            trigger[p : p + pulse_samples] = 48 if w % 2 == 0 else 16
+        end = t0 + 90 * sfreq
+        trigger[end : end + pulse_samples] = 255
+    rng = np.random.RandomState(42)
+    savemat(
+        path,
+        {
+            "EEG": rng.standard_normal((n_samples, 64)) * 10.0,  # microvolts
+            "EOG": rng.standard_normal((n_samples, 3)) * 10.0,
+            "Trigger": trigger,
+            "Header": {"fs": float(sfreq)},
+        },
+    )
+    return n_trajectories
+
+
+def _load_fake_2022_001_raw(tmp_path):
+    mat_path = str(tmp_path / "s1w.mat")
+    n_traj = _fake_2022_001_mat(mat_path)
+    ch_names = [f"EEG{i:02d}" for i in range(1, 65)] + ["EOG1", "EOG2", "EOG3"]
+    ch_types = ["eeg"] * 64 + ["eog"] * 3
+    raw = _convert_run_001_2022(mat_path, ch_names, ch_types, subject_id=1)
+    return raw, n_traj
+
+
+def test_bnci2022_001_trigger_pulses_annotated_once(tmp_path):
+    """Each held trigger pulse yields one annotation, not one per sample (gh-1143)."""
+    raw, n_traj = _load_fake_2022_001_raw(tmp_path)
+    desc = raw.annotations.description
+    assert np.sum(desc == "trajectory_start") == n_traj
+    assert np.sum(desc == "waypoint_hit") == 2 * n_traj
+    assert np.sum(desc == "waypoint_miss") == 2 * n_traj
+    assert np.sum(desc == "trajectory_end") == n_traj
+
+
+def test_bnci2022_001_default_epoching_is_bounded(tmp_path):
+    """Only the ~90 s trajectories are trials; point markers are not (gh-1143)."""
+    dataset = BNCI2022_001()
+    assert dataset.event_id == {"trajectory_start": 1}
+    assert dataset.interval == [0, 90]
+    raw, n_traj = _load_fake_2022_001_raw(tmp_path)
+    transform = SetRawAnnotations(dataset.event_id, interval=tuple(dataset.interval))
+    raw = transform.transform(raw)
+
+    trial_annotations = raw.annotations
+    assert set(trial_annotations.description) == {"trajectory_start"}
+    assert len(trial_annotations) == n_traj
+    # total epoched time cannot exceed the recording length
+    assert trial_annotations.duration.sum() <= raw.times[-1]
+
+
 def test_bnci2014_001_metadata():
     """Test metadata for BNCI2014-001."""
     dataset = BNCI2014_001()

moabb/tests/test_dataset_fixes.py

@@ -14,6 +14,7 @@
 from moabb.datasets.bnci.bnci_2020 import _convert_attention_shift
 from moabb.datasets.braininvaders import BI2015b
 from moabb.datasets.hefmi_ich2025 import HefmiIch2025
+from moabb.datasets.kaneshiro2015 import Kaneshiro2015
 from moabb.datasets.kojima2024a import Kojima2024A
 from moabb.datasets.mainsah2025 import _parse_manifest
 from moabb.datasets.schirrmeister2017 import Schirrmeister2017
@@ -301,3 +302,17 @@ def test_schirrmeister2017_reuses_relocated_files(tmp_path: Path, monkeypatch):
 
     dataset = Schirrmeister2017()
     assert dataset.data_path(1, path=str(tmp_path)) == relocated
+
+
+def test_kaneshiro2015_valid_for_declared_paradigm():
+    """Kaneshiro2015 is accepted by its declared paradigm (gh-1143): six object
+    categories, no Target/NonTarget, so "imagery" routes it to n-class paradigms."""
+    from moabb.paradigms import P300, Imagery, MotorImagery
+
+    dataset = Kaneshiro2015()
+    assert dataset.paradigm == "imagery"
+    for paradigm in (MotorImagery(), Imagery(), MotorImagery(n_classes=6)):
+        assert paradigm.is_valid(dataset)
+        assert paradigm.used_events(dataset) == dataset.event_id
+    # The old declaration was broken: P300 requires Target/NonTarget.
+    assert not P300().is_valid(dataset)