FIX Allow epoched EEGLAB files without events

doc/changes/dev/14163.bugfix.rst

@@ -0,0 +1 @@
+Allow :func:`mne.read_epochs_eeglab` to read epoched ``.set`` files without event information, by `Daria Agafonova`_.

mne/io/eeglab/eeglab.py

@@ -662,49 +662,65 @@ def __init__(
             event_name, event_latencies, unique_ev = list(), list(), list()
             ev_idx = 0
             warn_multiple_events = False
-            epochs = _bunchify(eeg.epoch)
-            events = _bunchify(eeg.event)
-            for ep in epochs:
-                if isinstance(ep.eventtype, int | float):
-                    ep.eventtype = str(ep.eventtype)
-                if not isinstance(ep.eventtype, str):
-                    event_type = "/".join([str(et) for et in ep.eventtype])
-                    event_name.append(event_type)
-                    # store latency of only first event
-                    # -1 to account for Matlab 1-based indexing of samples
-                    event_latencies.append(events[ev_idx].latency - 1)
-                    ev_idx += len(ep.eventtype)
-                    warn_multiple_events = True
-                else:
-                    event_type = ep.eventtype
-                    event_name.append(ep.eventtype)
-                    event_latencies.append(events[ev_idx].latency - 1)
-                    ev_idx += 1
-
-                if event_type not in unique_ev:
-                    unique_ev.append(event_type)
-
-                # invent event dict but use id > 0 so you know its a trigger
-                event_id = {ev: idx + 1 for idx, ev in enumerate(unique_ev)}
-
-            # warn about multiple events in epoch if necessary
-            if warn_multiple_events:
+            epochs = _bunchify(eeg.get("epoch", []))
+            eeg_events = _bunchify(eeg.get("event", []))
+            if len(epochs) == 0 or len(eeg_events) == 0:
                 warn(
-                    "At least one epoch has multiple events. Only the latency"
-                    " of the first event will be retained."
+                    "The EEGLAB file contains no event information. All epochs "
+                    "will be assigned to a single 'unknown' event."
                 )
+                event_id = {"unknown": 1}
+                events = np.column_stack(
+                    (
+                        np.arange(eeg.trials),
+                        np.zeros(eeg.trials, dtype=int),
+                        np.ones(eeg.trials, dtype=int),
+                    )
+                )
+            else:
+                for ep in epochs:
+                    if isinstance(ep.eventtype, int | float):
+                        ep.eventtype = str(ep.eventtype)
+                    if not isinstance(ep.eventtype, str):
+                        event_type = "/".join([str(et) for et in ep.eventtype])
+                        event_name.append(event_type)
+                        # store latency of only first event
+                        # -1 to account for Matlab 1-based indexing of samples
+                        event_latencies.append(eeg_events[ev_idx].latency - 1)
+                        ev_idx += len(ep.eventtype)
+                        warn_multiple_events = True
+                    else:
+                        event_type = ep.eventtype
+                        event_name.append(ep.eventtype)
+                        event_latencies.append(eeg_events[ev_idx].latency - 1)
+                        ev_idx += 1
+
+                    if event_type not in unique_ev:
+                        unique_ev.append(event_type)
+
+                    # invent event dict but use id > 0 so you know its a trigger
+                    event_id = {ev: idx + 1 for idx, ev in enumerate(unique_ev)}
+
+                # warn about multiple events in epoch if necessary
+                if warn_multiple_events:
+                    warn(
+                        "At least one epoch has multiple events. Only the latency"
+                        " of the first event will be retained."
+                    )
 
-            # now fill up the event array
-            events = np.zeros((eeg.trials, 3), dtype=int)
-            assert event_id is not None
-            for idx in range(0, eeg.trials):
-                if idx == 0:
-                    prev_stim = 0
-                elif idx > 0 and event_latencies[idx] - event_latencies[idx - 1] == 1:
-                    prev_stim = event_id[event_name[idx - 1]]
-                events[idx, 0] = event_latencies[idx]
-                events[idx, 1] = prev_stim
-                events[idx, 2] = event_id[event_name[idx]]
+                # now fill up the event array
+                events = np.zeros((eeg.trials, 3), dtype=int)
+                assert event_id is not None
+                for idx in range(0, eeg.trials):
+                    if idx == 0:
+                        prev_stim = 0
+                    elif (
+                        idx > 0 and event_latencies[idx] - event_latencies[idx - 1] == 1
+                    ):
+                        prev_stim = event_id[event_name[idx - 1]]
+                    events[idx, 0] = event_latencies[idx]
+                    events[idx, 1] = prev_stim
+                    events[idx, 2] = event_id[event_name[idx]]
         elif isinstance(events, str | Path | PathLike):
             events = read_events(events)
 

mne/io/eeglab/tests/test_eeglab.py

@@ -394,6 +394,45 @@ def test_io_set_epochs_events(tmp_path):
     pytest.raises(ValueError, read_epochs_eeglab, epochs_fname_mat, epochs.events, None)
 
 
+@pytest.mark.parametrize("include_event_fields", (True, False))
+def test_io_set_epochs_without_events(tmp_path, include_event_fields):
+    """Read epoched EEGLAB files that have no event information."""
+    n_epochs, n_channels, n_times = 3, 2, 20
+    data = np.arange(n_channels * n_times * n_epochs, dtype=float).reshape(
+        n_channels, n_times, n_epochs
+    )
+    fname = tmp_path / "no-events.set"
+    eeg = {
+        "trials": n_epochs,
+        "nbchan": n_channels,
+        "pnts": n_times,
+        "srate": 100.0,
+        "xmin": -0.1,
+        "xmax": 0.09,
+        "data": data,
+        "chanlocs": np.array(
+            [{"labels": "EEG 001"}, {"labels": "EEG 002"}], dtype=object
+        ),
+    }
+    if include_event_fields:
+        eeg.update(epoch=np.array([], dtype=object), event=np.array([], dtype=object))
+    io.savemat(fname, {"EEG": eeg}, appendmat=False)
+
+    with pytest.warns(RuntimeWarning, match="contains no event information"):
+        epochs = read_epochs_eeglab(fname)
+
+    expected_events = np.column_stack(
+        (
+            np.arange(n_epochs),
+            np.zeros(n_epochs, dtype=int),
+            np.ones(n_epochs, dtype=int),
+        )
+    )
+    assert epochs.event_id == {"unknown": 1}
+    assert_array_equal(epochs.events, expected_events)
+    assert_allclose(epochs.get_data(copy=False), data.transpose(2, 0, 1) * 1e-6)
+
+
 @testing.requires_testing_data
 @pytest.mark.filterwarnings("ignore:At least one epoch has multiple events")
 @pytest.mark.filterwarnings("ignore:The data contains 'boundary' events")