Add EEGBCI dataset and tasks

docs/api/datasets.rst

@@ -241,6 +241,7 @@ Available Datasets
     datasets/pyhealth.datasets.COVID19CXRDataset
     datasets/pyhealth.datasets.ChestXray14Dataset
     datasets/pyhealth.datasets.PhysioNetDeIDDataset
+    datasets/pyhealth.datasets.EEGBCIDataset
     datasets/pyhealth.datasets.TUABDataset
     datasets/pyhealth.datasets.TUEVDataset
     datasets/pyhealth.datasets.ClinVarDataset

docs/api/datasets/pyhealth.datasets.EEGBCIDataset.rst

@@ -0,0 +1,7 @@
+pyhealth.datasets.EEGBCIDataset
+================================
+
+.. autoclass:: pyhealth.datasets.EEGBCIDataset
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/tasks.rst

@@ -223,6 +223,7 @@ Available Tasks
     Sleep Staging <tasks/pyhealth.tasks.sleep_staging>
     Sleep Staging (SleepEDF) <tasks/pyhealth.tasks.SleepStagingSleepEDF>
     Temple University EEG Tasks <tasks/pyhealth.tasks.temple_university_EEG_tasks>
+    EEGBCI Tasks <tasks/pyhealth.tasks.eegbci>
     Sleep Staging v2 <tasks/pyhealth.tasks.sleep_staging_v2>
     Benchmark EHRShot <tasks/pyhealth.tasks.benchmark_ehrshot>
     ChestX-ray14 Binary Classification <tasks/pyhealth.tasks.ChestXray14BinaryClassification>

docs/api/tasks/pyhealth.tasks.eegbci.rst

@@ -0,0 +1,7 @@
+pyhealth.tasks.eegbci
+=====================
+
+.. automodule:: pyhealth.tasks.eegbci
+    :members:
+    :undoc-members:
+    :show-inheritance:

examples/eeg/eegbci/README.md

@@ -0,0 +1,56 @@
+# EEGBCI Pattern Discovery
+
+This example uses `EEGBCIDataset` and `EEGBCIPatternDiscovery` to create
+2-second EEGBCI windows with task labels, Welch bandpower features, and cautious
+frequency-profile interpretations.
+
+The interpretations are exploratory signal metadata. They are not clinical
+diagnoses and do not prove a subject's cognition.
+
+Run a tiny real-data example:
+
+```bash
+python examples/eeg/eegbci/eegbci_pattern_discovery.py \
+  --subjects 1 \
+  --runs 3 \
+  --max-windows 20 \
+  --download
+```
+
+Outputs are written to `outputs/eegbci_pattern_discovery/` by default:
+
+- `eegbci_pattern_windows.csv`
+- `eegbci_pattern_summary.md`
+
+The CSV has one row per emitted 2-second window. Key columns include subject/run
+metadata, `event_code`, decoded `task_label`, raw EEGBCI numeric label
+(`eegbci_label` / `label`), PyHealth model-local label (`model_label`),
+absolute window timing, band powers, relative band powers, `dominant_band`,
+frequency ratios, and `interpretation`.
+
+The moment-report columns add analysis-grade fields:
+
+- `analysis_version`
+- `state_hypothesis`, `state_confidence`, and `evidence_score`
+- `evidence_summary`
+- `rest_reference_scope` and rest-normalized relative band deltas
+- `task_state_relation`, `task_state_rationale`, and `task_state_confidence`
+- `is_low_confidence`, `is_possible_artifact`, and `is_mixed_or_ambiguous`
+
+The `interpretation` column is report-level text derived from these moment-report
+fields. Legacy task-level fields such as `brain_state_hypothesis`, `confidence`,
+and `quality_flags` are intentionally not written to the CSV.
+
+The Markdown report summarizes state counts, task-label/state agreement,
+rest-normalized bandpower deltas, confidence and quality flags, representative
+windows, limitations, and next checks. These labels are signal-pattern
+summaries from short EEG windows, not clinical findings or evidence of a
+subject's cognition.
+
+Implementation details are tracked in
+`docs/eeg_pattern_discovery/moment_report_implementation_plan.md`.
+
+`--root` points to the local EEGBCI data directory. With `--download`, MNE
+downloads any missing EDF files under that root. PyHealth task caches are stored
+under the configured PyHealth cache directory and are keyed by the requested
+subject/run selection.